{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-detection/papers/ran/6","list_of":"/task/object-detection","task":"Object Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":6,"pages_in_order":12,"rows_per_page":100,"rows":[501,600],"of":1183,"counts":{"archive_papers_tagged":10957,"with_a_code_link":4657,"where_syntology_ran_a_sample":1183,"not_listed_spam_title":0,"listed":10957,"listed_where_code_ran":1183,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":145,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":145,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-detection/papers/ran/1","prev":"/task/object-detection/papers/ran/5","next":"/task/object-detection/papers/ran/7","papers":[{"url":"/paper/bootstrapped-masked-autoencoders-for-vision","slug":"bootstrapped-masked-autoencoders-for-vision","title":"Bootstrapped Masked Autoencoders for Vision BERT Pretraining","date":"2022-07-14","arxiv_id":"2207.07116","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bootstrapped-masked-autoencoders-for-vision#ran","syntology_url":"https://syntology.ai/paper/2207.07116","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07116"}},"official":{"repos":["lightdxy/bootmae"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-deep-supervision","slug":"contrastive-deep-supervision","title":"Contrastive Deep Supervision","date":"2022-07-12","arxiv_id":"2207.05306","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-deep-supervision#ran","syntology_url":"https://syntology.ai/paper/2207.05306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05306"}},"official":{"repos":["archiplab-linfengzhang/contrastive-deep-supervision"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lightvit-towards-light-weight-convolution","slug":"lightvit-towards-light-weight-convolution","title":"LightViT: Towards Light-Weight Convolution-Free Vision Transformers","date":"2022-07-12","arxiv_id":"2207.05557","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lightvit-towards-light-weight-convolution#ran","syntology_url":"https://syntology.ai/paper/2207.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05557"}},"official":{"repos":["hunto/lightvit"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/wave-vit-unifying-wavelet-and-transformers","slug":"wave-vit-unifying-wavelet-and-transformers","title":"Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning","date":"2022-07-11","arxiv_id":"2207.04978","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wave-vit-unifying-wavelet-and-transformers#ran","syntology_url":"https://syntology.ai/paper/2207.04978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04978"}},"official":{"repos":["yehli/imagenetmodel"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-closer-look-at-invariances-in-self","slug":"a-closer-look-at-invariances-in-self","title":"A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision","date":"2022-07-11","arxiv_id":"2207.04997","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-closer-look-at-invariances-in-self#ran","syntology_url":"https://syntology.ai/paper/2207.04997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04997"}},"official":null}},{"url":"/paper/mix-teaching-a-simple-unified-and-effective","slug":"mix-teaching-a-simple-unified-and-effective","title":"Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection","date":"2022-07-10","arxiv_id":"2207.04448","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mix-teaching-a-simple-unified-and-effective#ran","syntology_url":"https://syntology.ai/paper/2207.04448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04448"}},"official":{"repos":["yanglei18/mix-teaching"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/should-all-proposals-be-treated-equally-in","slug":"should-all-proposals-be-treated-equally-in","title":"Should All Proposals be Treated Equally in Object Detection?","date":"2022-07-07","arxiv_id":"2207.03520","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/should-all-proposals-be-treated-equally-in#ran","syntology_url":"https://syntology.ai/paper/2207.03520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03520"}},"official":{"repos":["liyunsheng13/dpp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/chairs-can-be-stood-on-overcoming-object-bias","slug":"chairs-can-be-stood-on-overcoming-object-bias","title":"Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection","date":"2022-07-06","arxiv_id":"2207.02400","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/chairs-can-be-stood-on-overcoming-object-bias#ran","syntology_url":"https://syntology.ai/paper/2207.02400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02400"}},"official":{"repos":["daoyuan98/odm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-teacher-dense-pseudo-labels-for-semi","slug":"dense-teacher-dense-pseudo-labels-for-semi","title":"Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection","date":"2022-07-06","arxiv_id":"2207.02541","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dense-teacher-dense-pseudo-labels-for-semi#ran","syntology_url":"https://syntology.ai/paper/2207.02541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02541"}},"official":{"repos":["megvii-basedetection/denseteacher"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/yolov7-trainable-bag-of-freebies-sets-new","slug":"yolov7-trainable-bag-of-freebies-sets-new","title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","date":"2022-07-06","arxiv_id":"2207.02696","repositories_listed":21,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/yolov7-trainable-bag-of-freebies-sets-new#ran","syntology_url":"https://syntology.ai/paper/2207.02696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02696"}},"official":{"repos":["wongkinyiu/yolov7"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/sess-saliency-enhancing-with-scaling-and","slug":"sess-saliency-enhancing-with-scaling-and","title":"SESS: Saliency Enhancing with Scaling and Sliding","date":"2022-07-05","arxiv_id":"2207.01769","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/sess-saliency-enhancing-with-scaling-and#ran","syntology_url":"https://syntology.ai/paper/2207.01769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01769"}},"official":{"repos":["neouyghur/sess"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/open-vocabulary-multi-label-classification","slug":"open-vocabulary-multi-label-classification","title":"Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer","date":"2022-07-05","arxiv_id":"2207.01887","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/open-vocabulary-multi-label-classification#ran","syntology_url":"https://syntology.ai/paper/2207.01887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01887"}},"official":{"repos":["sunanhe/mkt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cobevt-cooperative-bird-s-eye-view-semantic","slug":"cobevt-cooperative-bird-s-eye-view-semantic","title":"CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers","date":"2022-07-05","arxiv_id":"2207.02202","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cobevt-cooperative-bird-s-eye-view-semantic#ran","syntology_url":"https://syntology.ai/paper/2207.02202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02202"}},"official":{"repos":["derrickxunu/cobevt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-autoencoders-for-self-supervised","slug":"masked-autoencoders-for-self-supervised","title":"Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds","date":"2022-07-01","arxiv_id":"2207.00531","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/masked-autoencoders-for-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2207.00531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.00531"}},"official":{"repos":["georghess/voxel-mae"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/polarformer-multi-camera-3d-object-detection","slug":"polarformer-multi-camera-3d-object-detection","title":"PolarFormer: Multi-camera 3D Object Detection with Polar Transformer","date":"2022-06-30","arxiv_id":"2206.15398","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/polarformer-multi-camera-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2206.15398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.15398"}},"official":{"repos":["fudan-zvg/polarformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/detecting-tiny-objects-in-aerial-images-a","slug":"detecting-tiny-objects-in-aerial-images-a","title":"Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark","date":"2022-06-28","arxiv_id":"2206.13996","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/detecting-tiny-objects-in-aerial-images-a#ran","syntology_url":"https://syntology.ai/paper/2206.13996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.13996"}},"official":{"repos":["Chasel-Tsui/mmdet-aitod"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revbifpn-the-fully-reversible-bidirectional","slug":"revbifpn-the-fully-reversible-bidirectional","title":"RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network","date":"2022-06-28","arxiv_id":"2206.14098","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revbifpn-the-fully-reversible-bidirectional#ran","syntology_url":"https://syntology.ai/paper/2206.14098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14098"}},"official":{"repos":["cerebrasresearch/revbifpn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/open-vocabulary-object-detection-with","slug":"open-vocabulary-object-detection-with","title":"Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization","date":"2022-06-22","arxiv_id":"2206.11134","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/open-vocabulary-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2206.11134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11134"}},"official":{"repos":["pealing/medet","peixianchen/medet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/behavior-transformers-cloning-k-modes-with","slug":"behavior-transformers-cloning-k-modes-with","title":"Behavior Transformers: Cloning $k$ modes with one stone","date":"2022-06-22","arxiv_id":"2206.11251","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/behavior-transformers-cloning-k-modes-with#ran","syntology_url":"https://syntology.ai/paper/2206.11251","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11251"}},"official":{"repos":["notmahi/bet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/scaling-up-kernels-in-3d-cnns","slug":"scaling-up-kernels-in-3d-cnns","title":"LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs","date":"2022-06-21","arxiv_id":"2206.10555","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scaling-up-kernels-in-3d-cnns#ran","syntology_url":"https://syntology.ai/paper/2206.10555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10555"}},"official":{"repos":["dvlab-research/largekernel3d"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/edgenext-efficiently-amalgamated-cnn","slug":"edgenext-efficiently-amalgamated-cnn","title":"EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications","date":"2022-06-21","arxiv_id":"2206.10589","repositories_listed":8,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/edgenext-efficiently-amalgamated-cnn#ran","syntology_url":"https://syntology.ai/paper/2206.10589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10589"}},"official":{"repos":["mmaaz60/EdgeNeXt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/voxel-mae-masked-autoencoders-for-pre","slug":"voxel-mae-masked-autoencoders-for-pre","title":"Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders","date":"2022-06-20","arxiv_id":"2206.09900","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/voxel-mae-masked-autoencoders-for-pre#ran","syntology_url":"https://syntology.ai/paper/2206.09900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09900"}},"official":{"repos":["chaytonmin/occupancy-mae","chaytonmin/voxel-mae"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/global-context-vision-transformers","slug":"global-context-vision-transformers","title":"Global Context Vision Transformers","date":"2022-06-20","arxiv_id":"2206.09959","repositories_listed":8,"syntology":{"n":36,"n_ran":21,"n_constructed":8,"n_ran_checked":17,"n_instrument":4,"n_unverified":15,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":15,"phrase":"21 ran (of which 8 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 15 unverified","sample_list":"/paper/global-context-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2206.09959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09959"}},"official":{"repos":["nvlabs/gcvit"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":8,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/patch-level-representation-learning-for-self-1","slug":"patch-level-representation-learning-for-self-1","title":"Patch-level Representation Learning for Self-supervised Vision Transformers","date":"2022-06-16","arxiv_id":"2206.07990","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/patch-level-representation-learning-for-self-1#ran","syntology_url":"https://syntology.ai/paper/2206.07990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07990"}},"official":{"repos":["alinlab/selfpatch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/monoground-detecting-monocular-3d-objects-1","slug":"monoground-detecting-monocular-3d-objects-1","title":"MonoGround: Detecting Monocular 3D Objects from the Ground","date":"2022-06-15","arxiv_id":"2206.07372","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/monoground-detecting-monocular-3d-objects-1#ran","syntology_url":"https://syntology.ai/paper/2206.07372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07372"}},"official":{"repos":["cfzd/monoground"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/coarse-to-fine-vision-language-pre-training","slug":"coarse-to-fine-vision-language-pre-training","title":"Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone","date":"2022-06-15","arxiv_id":"2206.07643","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/coarse-to-fine-vision-language-pre-training#ran","syntology_url":"https://syntology.ai/paper/2206.07643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07643"}},"official":{"repos":["microsoft/fiber"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/rf-next-efficient-receptive-field-search-for","slug":"rf-next-efficient-receptive-field-search-for","title":"RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks","date":"2022-06-14","arxiv_id":"2206.06637","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rf-next-efficient-receptive-field-search-for#ran","syntology_url":"https://syntology.ai/paper/2206.06637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06637"}},"official":{"repos":["ShangHua-Gao/RFNext"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-decoder-free-object-detection-with","slug":"efficient-decoder-free-object-detection-with","title":"Efficient Decoder-free Object Detection with Transformers","date":"2022-06-14","arxiv_id":"2206.06829","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-decoder-free-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2206.06829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06829"}},"official":{"repos":["Pealing/DFFT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/making-sense-of-dependence-efficient-black","slug":"making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","arxiv_id":"2206.06219","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/making-sense-of-dependence-efficient-black#ran","syntology_url":"https://syntology.ai/paper/2206.06219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06219"}},"official":{"repos":["paulnovello/hsic-attribution-method"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-domain-adaptive-object-detection","slug":"learning-domain-adaptive-object-detection","title":"Learning Domain Adaptive Object Detection with Probabilistic Teacher","date":"2022-06-13","arxiv_id":"2206.06293","repositories_listed":2,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-domain-adaptive-object-detection#ran","syntology_url":"https://syntology.ai/paper/2206.06293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06293"}},"official":{"repos":["hikvision-research/probabilisticteacher"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/glipv2-unifying-localization-and-vision","slug":"glipv2-unifying-localization-and-vision","title":"GLIPv2: Unifying Localization and Vision-Language Understanding","date":"2022-06-12","arxiv_id":"2206.05836","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glipv2-unifying-localization-and-vision#ran","syntology_url":"https://syntology.ai/paper/2206.05836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05836"}},"official":{"repos":["microsoft/GLIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-improved-one-millisecond-mobile-backbone","slug":"an-improved-one-millisecond-mobile-backbone","title":"MobileOne: An Improved One millisecond Mobile Backbone","date":"2022-06-08","arxiv_id":"2206.04040","repositories_listed":10,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-improved-one-millisecond-mobile-backbone#ran","syntology_url":"https://syntology.ai/paper/2206.04040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04040"}},"official":{"repos":["apple/ml-mobileone","rwightman/pytorch-image-models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tutel-adaptive-mixture-of-experts-at-scale","slug":"tutel-adaptive-mixture-of-experts-at-scale","title":"Tutel: Adaptive Mixture-of-Experts at Scale","date":"2022-06-07","arxiv_id":"2206.03382","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tutel-adaptive-mixture-of-experts-at-scale#ran","syntology_url":"https://syntology.ai/paper/2206.03382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.03382"}},"official":{"repos":["microsoft/Swin-Transformer","microsoft/tutel"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/separable-self-attention-for-mobile-vision","slug":"separable-self-attention-for-mobile-vision","title":"Separable Self-attention for Mobile Vision Transformers","date":"2022-06-06","arxiv_id":"2206.02680","repositories_listed":8,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/separable-self-attention-for-mobile-vision#ran","syntology_url":"https://syntology.ai/paper/2206.02680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02680"}},"official":{"repos":["apple/ml-cvnets"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/mask-dino-towards-a-unified-transformer-based-1","slug":"mask-dino-towards-a-unified-transformer-based-1","title":"Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation","date":"2022-06-06","arxiv_id":"2206.02777","repositories_listed":10,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":3,"n_instrument":8,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mask-dino-towards-a-unified-transformer-based-1#ran","syntology_url":"https://syntology.ai/paper/2206.02777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02777"}},"official":{"repos":["idea-research/maskdino"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/metrics-reloaded-pitfalls-and-recommendations","slug":"metrics-reloaded-pitfalls-and-recommendations","title":"Metrics reloaded: Recommendations for image analysis validation","date":"2022-06-03","arxiv_id":"2206.01653","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metrics-reloaded-pitfalls-and-recommendations#ran","syntology_url":"https://syntology.ai/paper/2206.01653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01653"}},"official":{"repos":["project-monai/metricsreloaded"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/petrv2-a-unified-framework-for-3d-perception","slug":"petrv2-a-unified-framework-for-3d-perception","title":"PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images","date":"2022-06-02","arxiv_id":"2206.01256","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/petrv2-a-unified-framework-for-3d-perception#ran","syntology_url":"https://syntology.ai/paper/2206.01256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01256"}},"official":{"repos":["megvii-research/petr"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/vision-gnn-an-image-is-worth-graph-of-nodes","slug":"vision-gnn-an-image-is-worth-graph-of-nodes","title":"Vision GNN: An Image is Worth Graph of Nodes","date":"2022-06-01","arxiv_id":"2206.00272","repositories_listed":9,"syntology":{"n":34,"n_ran":25,"n_constructed":6,"n_ran_checked":15,"n_instrument":10,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":22,"phrase":"25 ran (of which 6 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 10 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/vision-gnn-an-image-is-worth-graph-of-nodes#ran","syntology_url":"https://syntology.ai/paper/2206.00272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00272"}},"official":{"repos":["huawei-noah/CV-backbones","huawei-noah/efficient-ai-backbones"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["community","listed"]}}},{"url":"/paper/transfuser-imitation-with-transformer-based","slug":"transfuser-imitation-with-transformer-based","title":"TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving","date":"2022-05-31","arxiv_id":"2205.15997","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transfuser-imitation-with-transformer-based#ran","syntology_url":"https://syntology.ai/paper/2205.15997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15997"}},"official":{"repos":["autonomousvision/transfuser"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/illumination-adaptive-transformer","slug":"illumination-adaptive-transformer","title":"You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction","date":"2022-05-30","arxiv_id":"2205.14871","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/illumination-adaptive-transformer#ran","syntology_url":"https://syntology.ai/paper/2205.14871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14871"}},"official":{"repos":["cuiziteng/illumination-adaptive-transformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-the-robustness-of-lidar-camera","slug":"benchmarking-the-robustness-of-lidar-camera","title":"Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection","date":"2022-05-30","arxiv_id":"2205.14951","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/benchmarking-the-robustness-of-lidar-camera#ran","syntology_url":"https://syntology.ai/paper/2205.14951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14951"}},"official":{"repos":["kcyu2014/lidar-camera-robust-benchmark"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-visual-representation-2","slug":"self-supervised-visual-representation-2","title":"Self-Supervised Visual Representation Learning with Semantic Grouping","date":"2022-05-30","arxiv_id":"2205.15288","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-visual-representation-2#ran","syntology_url":"https://syntology.ai/paper/2205.15288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15288"}},"official":{"repos":["CVMI-Lab/SlotCon"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-distillation-with-receptive-tokens","slug":"masked-distillation-with-receptive-tokens","title":"Masked Distillation with Receptive Tokens","date":"2022-05-29","arxiv_id":"2205.14589","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-distillation-with-receptive-tokens#ran","syntology_url":"https://syntology.ai/paper/2205.14589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14589"}},"official":{"repos":["hunto/maskd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficientvit-enhanced-linear-attention-for","slug":"efficientvit-enhanced-linear-attention-for","title":"EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction","date":"2022-05-29","arxiv_id":"2205.14756","repositories_listed":6,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficientvit-enhanced-linear-attention-for#ran","syntology_url":"https://syntology.ai/paper/2205.14756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14756"}},"official":{"repos":["mit-han-lab/efficientvit","rwightman/pytorch-image-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bevfusion-a-simple-and-robust-lidar-camera","slug":"bevfusion-a-simple-and-robust-lidar-camera","title":"BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework","date":"2022-05-27","arxiv_id":"2205.13790","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bevfusion-a-simple-and-robust-lidar-camera#ran","syntology_url":"https://syntology.ai/paper/2205.13790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13790"}},"official":{"repos":["adlab-autodrive/bevfusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-learning-rivals-masked-image","slug":"contrastive-learning-rivals-masked-image","title":"Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation","date":"2022-05-27","arxiv_id":"2205.14141","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/contrastive-learning-rivals-masked-image#ran","syntology_url":"https://syntology.ai/paper/2205.14141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14141"}},"official":{"repos":["SwinTransformer/Feature-Distillation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/trainable-weight-averaging-for-fast","slug":"trainable-weight-averaging-for-fast","title":"Trainable Weight Averaging: A General Approach for Subspace Training","date":"2022-05-26","arxiv_id":"2205.13104","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":5,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 5 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/trainable-weight-averaging-for-fast#ran","syntology_url":"https://syntology.ai/paper/2205.13104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13104"}},"official":{"repos":["nblt/twa"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-vision-transformers-with-hilo-attention","slug":"fast-vision-transformers-with-hilo-attention","title":"Fast Vision Transformers with HiLo Attention","date":"2022-05-26","arxiv_id":"2205.13213","repositories_listed":5,"syntology":{"n":13,"n_ran":11,"n_constructed":1,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":2,"phrase":"11 ran (of which 1 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fast-vision-transformers-with-hilo-attention#ran","syntology_url":"https://syntology.ai/paper/2205.13213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13213"}},"official":{"repos":["zip-group/litv2","ziplab/litv2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/green-hierarchical-vision-transformer-for","slug":"green-hierarchical-vision-transformer-for","title":"Green Hierarchical Vision Transformer for Masked Image Modeling","date":"2022-05-26","arxiv_id":"2205.13515","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/green-hierarchical-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2205.13515","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13515"}},"official":{"repos":["layneh/greenmim"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bevfusion-multi-task-multi-sensor-fusion-with","slug":"bevfusion-multi-task-multi-sensor-fusion-with","title":"BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation","date":"2022-05-26","arxiv_id":"2205.13542","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bevfusion-multi-task-multi-sensor-fusion-with#ran","syntology_url":"https://syntology.ai/paper/2205.13542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13542"}},"official":{"repos":["mit-han-lab/bevfusion"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/selfreformer-self-refined-network-with","slug":"selfreformer-self-refined-network-with","title":"SelfReformer: Self-Refined Network with Transformer for Salient Object Detection","date":"2022-05-23","arxiv_id":"2205.11283","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/selfreformer-self-refined-network-with#ran","syntology_url":"https://syntology.ai/paper/2205.11283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11283"}},"official":{"repos":["BarCodeReader/SelfReformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-distillation-from-a-stronger","slug":"knowledge-distillation-from-a-stronger","title":"Knowledge Distillation from A Stronger Teacher","date":"2022-05-21","arxiv_id":"2205.10536","repositories_listed":3,"syntology":{"n":11,"n_ran":10,"n_constructed":1,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":4,"n_violates":4,"n_no_contract":2,"n_pointer_only":0,"phrase":"10 ran (of which 1 constructed an object rather than computing a result; 10 with no instrument failure: 4 honoured, 4 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/knowledge-distillation-from-a-stronger#ran","syntology_url":"https://syntology.ai/paper/2205.10536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10536"}},"official":{"repos":["hunto/dist_kd"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/towards-lossless-ann-snn-conversion-under","slug":"towards-lossless-ann-snn-conversion-under","title":"Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization","date":"2022-05-16","arxiv_id":"2205.07473","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-lossless-ann-snn-conversion-under#ran","syntology_url":"https://syntology.ai/paper/2205.07473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07473"}},"official":{"repos":["Windere/snn-cvt-dual-phase"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/promoting-saliency-from-depth-deep-1","slug":"promoting-saliency-from-depth-deep-1","title":"Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection","date":"2022-05-15","arxiv_id":"2205.07179","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/promoting-saliency-from-depth-deep-1#ran","syntology_url":"https://syntology.ai/paper/2205.07179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07179"}},"official":{"repos":["jiwei0921/dsu"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/object-detection-with-spiking-neural-networks","slug":"object-detection-with-spiking-neural-networks","title":"Object Detection with Spiking Neural Networks on Automotive Event Data","date":"2022-05-09","arxiv_id":"2205.04339","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/object-detection-with-spiking-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2205.04339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.04339"}},"official":{"repos":["loiccordone/object-detection-with-spiking-neural-networks"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/convmae-masked-convolution-meets-masked","slug":"convmae-masked-convolution-meets-masked","title":"ConvMAE: Masked Convolution Meets Masked Autoencoders","date":"2022-05-08","arxiv_id":"2205.03892","repositories_listed":5,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/convmae-masked-convolution-meets-masked#ran","syntology_url":"https://syntology.ai/paper/2205.03892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03892"}},"official":{"repos":["alpha-vl/convmae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/multitask-aet-with-orthogonal-tangent-1","slug":"multitask-aet-with-orthogonal-tangent-1","title":"Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection","date":"2022-05-06","arxiv_id":"2205.03346","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multitask-aet-with-orthogonal-tangent-1#ran","syntology_url":"https://syntology.ai/paper/2205.03346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03346"}},"official":{"repos":["cuiziteng/iccv_maet","cuiziteng/maet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-object-detection-with-a-self-supervised","slug":"3d-object-detection-with-a-self-supervised","title":"3D Object Detection with a Self-supervised Lidar Scene Flow Backbone","date":"2022-05-02","arxiv_id":"2205.00705","repositories_listed":2,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/3d-object-detection-with-a-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2205.00705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00705"}},"official":{"repos":["emecercelik/ssl-3d-detection"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/rotationally-equivariant-3d-object-detection","slug":"rotationally-equivariant-3d-object-detection","title":"Rotationally Equivariant 3D Object Detection","date":"2022-04-28","arxiv_id":"2204.13630","repositories_listed":0,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rotationally-equivariant-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2204.13630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.13630"}},"official":null}},{"url":"/paper/where-and-what-driver-attention-based-object","slug":"where-and-what-driver-attention-based-object","title":"Where and What: Driver Attention-based Object Detection","date":"2022-04-26","arxiv_id":"2204.12150","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/where-and-what-driver-attention-based-object#ran","syntology_url":"https://syntology.ai/paper/2204.12150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12150"}},"official":{"repos":["yaorong0921/driver-gaze-yolov5"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/focal-sparse-convolutional-networks-for-3d","slug":"focal-sparse-convolutional-networks-for-3d","title":"Focal Sparse Convolutional Networks for 3D Object Detection","date":"2022-04-26","arxiv_id":"2204.12463","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/focal-sparse-convolutional-networks-for-3d#ran","syntology_url":"https://syntology.ai/paper/2204.12463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12463"}},"official":{"repos":["dvlab-research/focalsconv"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/polyloss-a-polynomial-expansion-perspective-1","slug":"polyloss-a-polynomial-expansion-perspective-1","title":"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions","date":"2022-04-26","arxiv_id":"2204.12511","repositories_listed":19,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/polyloss-a-polynomial-expansion-perspective-1#ran","syntology_url":"https://syntology.ai/paper/2204.12511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12511"}},"official":null}},{"url":"/paper/fast-advprop-1","slug":"fast-advprop-1","title":"Fast AdvProp","date":"2022-04-21","arxiv_id":"2204.09838","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-advprop-1#ran","syntology_url":"https://syntology.ai/paper/2204.09838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09838"}},"official":{"repos":["meijieru/fast_advprop"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/k-lite-learning-transferable-visual-models","slug":"k-lite-learning-transferable-visual-models","title":"K-LITE: Learning Transferable Visual Models with External Knowledge","date":"2022-04-20","arxiv_id":"2204.09222","repositories_listed":2,"syntology":{"n":10,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/k-lite-learning-transferable-visual-models#ran","syntology_url":"https://syntology.ai/paper/2204.09222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09222"}},"official":null}},{"url":"/paper/multimodal-token-fusion-for-vision","slug":"multimodal-token-fusion-for-vision","title":"Multimodal Token Fusion for Vision Transformers","date":"2022-04-19","arxiv_id":"2204.08721","repositories_listed":11,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multimodal-token-fusion-for-vision#ran","syntology_url":"https://syntology.ai/paper/2204.08721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08721"}},"official":{"repos":["yikaiw/TokenFusion","huawei-noah/noah-research","mindspore-ai/models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/elevater-a-benchmark-and-toolkit-for","slug":"elevater-a-benchmark-and-toolkit-for","title":"ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models","date":"2022-04-19","arxiv_id":"2204.08790","repositories_listed":9,"syntology":{"n":20,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/elevater-a-benchmark-and-toolkit-for#ran","syntology_url":"https://syntology.ai/paper/2204.08790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08790"}},"official":{"repos":["Computer-Vision-in-the-Wild/Elevater_Toolkit_IC","computer-vision-in-the-wild/cvinw_readings"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/vsa-learning-varied-size-window-attention-in","slug":"vsa-learning-varied-size-window-attention-in","title":"VSA: Learning Varied-Size Window Attention in Vision Transformers","date":"2022-04-18","arxiv_id":"2204.08446","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/vsa-learning-varied-size-window-attention-in#ran","syntology_url":"https://syntology.ai/paper/2204.08446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08446"}},"official":{"repos":["vitae-transformer/vitae-vsa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/an-extendable-efficient-and-effective","slug":"an-extendable-efficient-and-effective","title":"An Extendable, Efficient and Effective Transformer-based Object Detector","date":"2022-04-17","arxiv_id":"2204.07962","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-extendable-efficient-and-effective#ran","syntology_url":"https://syntology.ai/paper/2204.07962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07962"}},"official":{"repos":["naver-ai/vidt"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["community","official"]}}},{"url":"/paper/entropy-based-active-learning-for-object","slug":"entropy-based-active-learning-for-object","title":"Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint","date":"2022-04-17","arxiv_id":"2204.07965","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/entropy-based-active-learning-for-object#ran","syntology_url":"https://syntology.ai/paper/2204.07965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07965"}},"official":null}},{"url":"/paper/neighborhood-attention-transformer","slug":"neighborhood-attention-transformer","title":"Neighborhood Attention Transformer","date":"2022-04-14","arxiv_id":"2204.07143","repositories_listed":5,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neighborhood-attention-transformer#ran","syntology_url":"https://syntology.ai/paper/2204.07143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07143"}},"official":{"repos":["SHI-Labs/Neighborhood-Attention-Transformer","shi-labs/natten"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/occam-s-laser-occlusion-based-attribution","slug":"occam-s-laser-occlusion-based-attribution","title":"OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data","date":"2022-04-13","arxiv_id":"2204.06577","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/occam-s-laser-occlusion-based-attribution#ran","syntology_url":"https://syntology.ai/paper/2204.06577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06577"}},"official":{"repos":["dschinagl/occam"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dair-v2x-a-large-scale-dataset-for-vehicle","slug":"dair-v2x-a-large-scale-dataset-for-vehicle","title":"DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection","date":"2022-04-12","arxiv_id":"2204.05575","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/dair-v2x-a-large-scale-dataset-for-vehicle#ran","syntology_url":"https://syntology.ai/paper/2204.05575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05575"}},"official":{"repos":["air-thu/dair-v2x"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pyramid-grafting-network-for-one-stage-high","slug":"pyramid-grafting-network-for-one-stage-high","title":"Pyramid Grafting Network for One-Stage High Resolution Saliency Detection","date":"2022-04-11","arxiv_id":"2204.05041","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pyramid-grafting-network-for-one-stage-high#ran","syntology_url":"https://syntology.ai/paper/2204.05041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05041"}},"official":{"repos":["icvteam/pgnet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/davit-dual-attention-vision-transformers","slug":"davit-dual-attention-vision-transformers","title":"DaViT: Dual Attention Vision Transformers","date":"2022-04-07","arxiv_id":"2204.03645","repositories_listed":4,"syntology":{"n":15,"n_ran":8,"n_constructed":5,"n_ran_checked":6,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/davit-dual-attention-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2204.03645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03645"}},"official":{"repos":["dingmyu/davit"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/overcoming-catastrophic-forgetting-in-1","slug":"overcoming-catastrophic-forgetting-in-1","title":"Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation","date":"2022-04-05","arxiv_id":"2204.02136","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/overcoming-catastrophic-forgetting-in-1#ran","syntology_url":"https://syntology.ai/paper/2204.02136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02136"}},"official":{"repos":["hi-ft/erd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/maxvit-multi-axis-vision-transformer","slug":"maxvit-multi-axis-vision-transformer","title":"MaxViT: Multi-Axis Vision Transformer","date":"2022-04-04","arxiv_id":"2204.01697","repositories_listed":15,"syntology":{"n":53,"n_ran":37,"n_constructed":15,"n_ran_checked":23,"n_instrument":14,"n_unverified":16,"n_honours":2,"n_violates":0,"n_no_contract":21,"n_pointer_only":9,"phrase":"37 ran (of which 15 constructed an object rather than computing a result; 23 with no instrument failure: 2 honoured, 0 violated, 21 with no contract checked; 14 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/maxvit-multi-axis-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2204.01697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01697"}},"official":{"repos":["google-research/maxvit"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/online-convolutional-re-parameterization","slug":"online-convolutional-re-parameterization","title":"Online Convolutional Re-parameterization","date":"2022-04-02","arxiv_id":"2204.00826","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/online-convolutional-re-parameterization#ran","syntology_url":"https://syntology.ai/paper/2204.00826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00826"}},"official":{"repos":["jugghm/orepa_cvpr2022"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-zero-shot-hoi-detection-via-vision","slug":"end-to-end-zero-shot-hoi-detection-via-vision","title":"End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation","date":"2022-04-01","arxiv_id":"2204.03541","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/end-to-end-zero-shot-hoi-detection-via-vision#ran","syntology_url":"https://syntology.ai/paper/2204.03541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03541"}},"official":{"repos":["mrwu-mac/EoID"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pp-yoloe-an-evolved-version-of-yolo","slug":"pp-yoloe-an-evolved-version-of-yolo","title":"PP-YOLOE: An evolved version of YOLO","date":"2022-03-30","arxiv_id":"2203.16250","repositories_listed":8,"syntology":{"n":27,"n_ran":22,"n_constructed":0,"n_ran_checked":21,"n_instrument":1,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":19,"n_pointer_only":1,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 2 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/pp-yoloe-an-evolved-version-of-yolo#ran","syntology_url":"https://syntology.ai/paper/2203.16250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16250"}},"official":{"repos":["PaddlePaddle/PaddleDetection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/image-to-lidar-self-supervised-distillation","slug":"image-to-lidar-self-supervised-distillation","title":"Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data","date":"2022-03-30","arxiv_id":"2203.16258","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-to-lidar-self-supervised-distillation#ran","syntology_url":"https://syntology.ai/paper/2203.16258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16258"}},"official":{"repos":["valeoai/slidr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tubedetr-spatio-temporal-video-grounding-with","slug":"tubedetr-spatio-temporal-video-grounding-with","title":"TubeDETR: Spatio-Temporal Video Grounding with Transformers","date":"2022-03-30","arxiv_id":"2203.16434","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/tubedetr-spatio-temporal-video-grounding-with#ran","syntology_url":"https://syntology.ai/paper/2203.16434","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16434"}},"official":{"repos":["antoyang/TubeDETR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adamixer-a-fast-converging-query-based-object","slug":"adamixer-a-fast-converging-query-based-object","title":"AdaMixer: A Fast-Converging Query-Based Object Detector","date":"2022-03-30","arxiv_id":"2203.16507","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adamixer-a-fast-converging-query-based-object#ran","syntology_url":"https://syntology.ai/paper/2203.16507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16507"}},"official":{"repos":["mcg-nju/adamixer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-transformers-for-grounded","slug":"collaborative-transformers-for-grounded","title":"Collaborative Transformers for Grounded Situation Recognition","date":"2022-03-30","arxiv_id":"2203.16518","repositories_listed":3,"syntology":{"n":7,"n_ran":5,"n_constructed":3,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/collaborative-transformers-for-grounded#ran","syntology_url":"https://syntology.ai/paper/2203.16518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16518"}},"official":{"repos":["jhcho99/coformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/exploring-plain-vision-transformer-backbones","slug":"exploring-plain-vision-transformer-backbones","title":"Exploring Plain Vision Transformer Backbones for Object Detection","date":"2022-03-30","arxiv_id":"2203.16527","repositories_listed":11,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-plain-vision-transformer-backbones#ran","syntology_url":"https://syntology.ai/paper/2203.16527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16527"}},"official":{"repos":["facebookresearch/detectron2"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/siod-single-instance-annotated-per-category","slug":"siod-single-instance-annotated-per-category","title":"SIOD: Single Instance Annotated Per Category Per Image for Object Detection","date":"2022-03-29","arxiv_id":"2203.15353","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/siod-single-instance-annotated-per-category#ran","syntology_url":"https://syntology.ai/paper/2203.15353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15353"}},"official":{"repos":["solicucu/siod"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mc-beit-multi-choice-discretization-for-image","slug":"mc-beit-multi-choice-discretization-for-image","title":"mc-BEiT: Multi-choice Discretization for Image BERT Pre-training","date":"2022-03-29","arxiv_id":"2203.15371","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mc-beit-multi-choice-discretization-for-image#ran","syntology_url":"https://syntology.ai/paper/2203.15371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15371"}},"official":{"repos":["lixiaotong97/mc-beit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sepvit-separable-vision-transformer","slug":"sepvit-separable-vision-transformer","title":"SepViT: Separable Vision Transformer","date":"2022-03-29","arxiv_id":"2203.15380","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sepvit-separable-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2203.15380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15380"}},"official":{"repos":["liwei109/sepvit"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/chex-channel-exploration-for-cnn-model","slug":"chex-channel-exploration-for-cnn-model","title":"CHEX: CHannel EXploration for CNN Model Compression","date":"2022-03-29","arxiv_id":"2203.15794","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chex-channel-exploration-for-cnn-model#ran","syntology_url":"https://syntology.ai/paper/2203.15794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15794"}},"official":null}},{"url":"/paper/learning-where-to-learn-in-cross-view-self","slug":"learning-where-to-learn-in-cross-view-self","title":"Learning Where to Learn in Cross-View Self-Supervised Learning","date":"2022-03-28","arxiv_id":"2203.14898","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-where-to-learn-in-cross-view-self#ran","syntology_url":"https://syntology.ai/paper/2203.14898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14898"}},"official":{"repos":["LayneH/LEWEL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-prompt-for-open-vocabulary-object","slug":"learning-to-prompt-for-open-vocabulary-object","title":"Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model","date":"2022-03-28","arxiv_id":"2203.14940","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-prompt-for-open-vocabulary-object#ran","syntology_url":"https://syntology.ai/paper/2203.14940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14940"}},"official":{"repos":["dyabel/detpro"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lidar-distillation-bridging-the-beam-induced","slug":"lidar-distillation-bridging-the-beam-induced","title":"LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection","date":"2022-03-28","arxiv_id":"2203.14956","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lidar-distillation-bridging-the-beam-induced#ran","syntology_url":"https://syntology.ai/paper/2203.14956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14956"}},"official":{"repos":["weiyithu/lidar-distillation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/observation-centric-sort-rethinking-sort-for","slug":"observation-centric-sort-rethinking-sort-for","title":"Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking","date":"2022-03-27","arxiv_id":"2203.14360","repositories_listed":7,"syntology":{"n":31,"n_ran":27,"n_constructed":0,"n_ran_checked":24,"n_instrument":3,"n_unverified":4,"n_honours":9,"n_violates":0,"n_no_contract":15,"n_pointer_only":13,"phrase":"27 ran (of which 0 constructed an object rather than computing a result; 24 with no instrument failure: 9 honoured, 0 violated, 15 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/observation-centric-sort-rethinking-sort-for#ran","syntology_url":"https://syntology.ai/paper/2203.14360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14360"}},"official":{"repos":["noahcao/OC_SORT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/point2seq-detecting-3d-objects-as-sequences","slug":"point2seq-detecting-3d-objects-as-sequences","title":"Point2Seq: Detecting 3D Objects as Sequences","date":"2022-03-25","arxiv_id":"2203.13394","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":4,"n_ran_checked":8,"n_instrument":5,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"13 ran (of which 4 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/point2seq-detecting-3d-objects-as-sequences#ran","syntology_url":"https://syntology.ai/paper/2203.13394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13394"}},"official":{"repos":["ocnflag/point2seq"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":4,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/sparse-instance-activation-for-real-time","slug":"sparse-instance-activation-for-real-time","title":"Sparse Instance Activation for Real-Time Instance Segmentation","date":"2022-03-24","arxiv_id":"2203.12827","repositories_listed":2,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparse-instance-activation-for-real-time#ran","syntology_url":"https://syntology.ai/paper/2203.12827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12827"}},"official":{"repos":["hustvl/sparseinst"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/real-time-object-detection-for-streaming","slug":"real-time-object-detection-for-streaming","title":"Real-time Object Detection for Streaming Perception","date":"2022-03-23","arxiv_id":"2203.12338","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/real-time-object-detection-for-streaming#ran","syntology_url":"https://syntology.ai/paper/2203.12338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12338"}},"official":{"repos":["yancie-yjr/StreamYOLO"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-salient-object-detection-with","slug":"unsupervised-salient-object-detection-with","title":"Unsupervised Salient Object Detection with Spectral Cluster Voting","date":"2022-03-23","arxiv_id":"2203.12614","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/unsupervised-salient-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2203.12614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12614"}},"official":{"repos":["noelshin/selfmask"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/hindsight-is-20-20-leveraging-past-traversals-1","slug":"hindsight-is-20-20-leveraging-past-traversals-1","title":"Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception","date":"2022-03-22","arxiv_id":"2203.11405","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hindsight-is-20-20-leveraging-past-traversals-1#ran","syntology_url":"https://syntology.ai/paper/2203.11405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11405"}},"official":{"repos":["yurongyou/hindsight"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/transfusion-robust-lidar-camera-fusion-for-3d","slug":"transfusion-robust-lidar-camera-fusion-for-3d","title":"TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers","date":"2022-03-22","arxiv_id":"2203.11496","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transfusion-robust-lidar-camera-fusion-for-3d#ran","syntology_url":"https://syntology.ai/paper/2203.11496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11496"}},"official":{"repos":["xuyangbai/transfusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/open-vocabulary-detr-with-conditional","slug":"open-vocabulary-detr-with-conditional","title":"Open-Vocabulary DETR with Conditional Matching","date":"2022-03-22","arxiv_id":"2203.11876","repositories_listed":4,"syntology":{"n":8,"n_ran":6,"n_constructed":2,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/open-vocabulary-detr-with-conditional#ran","syntology_url":"https://syntology.ai/paper/2203.11876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11876"}},"official":{"repos":["yuhangzang/ov-detr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/focal-modulation-networks","slug":"focal-modulation-networks","title":"Focal Modulation Networks","date":"2022-03-22","arxiv_id":"2203.11926","repositories_listed":9,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/focal-modulation-networks#ran","syntology_url":"https://syntology.ai/paper/2203.11926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11926"}},"official":{"repos":["microsoft/FocalNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"8f839b123a06721c3bb63391ccaec0fc7d0f5bc7ee941155c2ade7b7b491977c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}