{"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/3","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":110,"rows_per_page":100,"rows":[201,300],"of":10957,"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","prev":"/task/object-detection/papers/2","next":"/task/object-detection/papers/4","papers":[{"url":"/paper/detectors-detecting-objects-with-recursive-1","slug":"detectors-detecting-objects-with-recursive-1","title":"DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution","date":"2020-06-03","arxiv_id":"2006.02334","repositories_listed":6,"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":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) · 1 unverified","sample_list":"/paper/detectors-detecting-objects-with-recursive-1#ran","syntology_url":"https://syntology.ai/paper/2006.02334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02334"}},"official":{"repos":["joe-siyuan-qiao/DetectoRS"],"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/sp-nas-serial-to-parallel-backbone-search-for","slug":"sp-nas-serial-to-parallel-backbone-search-for","title":"SP-NAS: Serial-to-Parallel Backbone Search for Object Detection","date":"2020-06-01","arxiv_id":null,"repositories_listed":6,"syntology":null},{"url":"/paper/enhancing-geometric-factors-in-model-learning","slug":"enhancing-geometric-factors-in-model-learning","title":"Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation","date":"2020-05-07","arxiv_id":"2005.03572","repositories_listed":6,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/enhancing-geometric-factors-in-model-learning#ran","syntology_url":"https://syntology.ai/paper/2005.03572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.03572"}},"official":{"repos":["Zzh-tju/CIoU"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/reverse-attention-based-residual-network-for","slug":"reverse-attention-based-residual-network-for","title":"Reverse Attention-Based Residual Network for Salient Object Detection","date":"2020-01-22","arxiv_id":null,"repositories_listed":6,"syntology":null},{"url":"/paper/cbnet-a-novel-composite-backbone-network","slug":"cbnet-a-novel-composite-backbone-network","title":"CBNet: A Novel Composite Backbone Network Architecture for Object Detection","date":"2019-09-09","arxiv_id":"1909.03625","repositories_listed":6,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/cbnet-a-novel-composite-backbone-network#ran","syntology_url":"https://syntology.ai/paper/1909.03625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.03625"}},"official":{"repos":["PKUbahuangliuhe/CBNet"],"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/training-time-friendly-network-for-real-time","slug":"training-time-friendly-network-for-real-time","title":"Training-Time-Friendly Network for Real-Time Object Detection","date":"2019-09-02","arxiv_id":"1909.00700","repositories_listed":6,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/training-time-friendly-network-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1909.00700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00700"}},"official":{"repos":["ZJULearning/ttfnet"],"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/part-a2-net-3d-part-aware-and-aggregation","slug":"part-a2-net-3d-part-aware-and-aggregation","title":"From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network","date":"2019-07-08","arxiv_id":"1907.03670","repositories_listed":6,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"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, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/part-a2-net-3d-part-aware-and-aggregation#ran","syntology_url":"https://syntology.ai/paper/1907.03670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.03670"}},"official":{"repos":["sshaoshuai/PointCloudDet3D"],"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/learning-data-augmentation-strategies-for","slug":"learning-data-augmentation-strategies-for","title":"Learning Data Augmentation Strategies for Object Detection","date":"2019-06-26","arxiv_id":"1906.11172","repositories_listed":6,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-data-augmentation-strategies-for#ran","syntology_url":"https://syntology.ai/paper/1906.11172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.11172"}},"official":{"repos":["tensorflow/tpu"],"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/unsupervised-learning-of-object-keypoints-for","slug":"unsupervised-learning-of-object-keypoints-for","title":"Unsupervised Learning of Object Keypoints for Perception and Control","date":"2019-06-19","arxiv_id":"1906.11883","repositories_listed":6,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"10 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unsupervised-learning-of-object-keypoints-for#ran","syntology_url":"https://syntology.ai/paper/1906.11883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.11883"}},"official":{"repos":["deepmind/deepmind-research"],"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/reppoints-point-set-representation-for-object","slug":"reppoints-point-set-representation-for-object","title":"RepPoints: Point Set Representation for Object Detection","date":"2019-04-25","arxiv_id":"1904.11490","repositories_listed":6,"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":3,"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/reppoints-point-set-representation-for-object#ran","syntology_url":"https://syntology.ai/paper/1904.11490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.11490"}},"official":{"repos":["microsoft/RepPoints"],"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/190408900","slug":"190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","arxiv_id":"1904.08900","repositories_listed":6,"syntology":{"n":27,"n_ran":21,"n_constructed":0,"n_ran_checked":21,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":20,"n_pointer_only":0,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 1 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/190408900#ran","syntology_url":"https://syntology.ai/paper/1904.08900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08900"}},"official":{"repos":["princeton-vl/CornerNet-Lite"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/libra-r-cnn-towards-balanced-learning-for","slug":"libra-r-cnn-towards-balanced-learning-for","title":"Libra R-CNN: Towards Balanced Learning for Object Detection","date":"2019-04-04","arxiv_id":"1904.02701","repositories_listed":6,"syntology":null},{"url":"/paper/retina-u-net-embarrassingly-simple","slug":"retina-u-net-embarrassingly-simple","title":"Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection","date":"2018-11-21","arxiv_id":"1811.08661","repositories_listed":6,"syntology":null},{"url":"/paper/relation-networks-for-object-detection","slug":"relation-networks-for-object-detection","title":"Relation Networks for Object Detection","date":"2017-11-30","arxiv_id":"1711.11575","repositories_listed":6,"syntology":null},{"url":"/paper/dota-a-large-scale-dataset-for-object","slug":"dota-a-large-scale-dataset-for-object","title":"DOTA: A Large-scale Dataset for Object Detection in Aerial Images","date":"2017-11-28","arxiv_id":"1711.10398","repositories_listed":6,"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":4,"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/dota-a-large-scale-dataset-for-object#ran","syntology_url":"https://syntology.ai/paper/1711.10398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10398"}},"official":null}},{"url":"/paper/megdet-a-large-mini-batch-object-detector","slug":"megdet-a-large-mini-batch-object-detector","title":"MegDet: A Large Mini-Batch Object Detector","date":"2017-11-20","arxiv_id":"1711.07240","repositories_listed":6,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 3 unverified","sample_list":"/paper/megdet-a-large-mini-batch-object-detector#ran","syntology_url":"https://syntology.ai/paper/1711.07240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.07240"}},"official":null}},{"url":"/paper/cut-paste-and-learn-surprisingly-easy","slug":"cut-paste-and-learn-surprisingly-easy","title":"Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection","date":"2017-08-04","arxiv_id":"1708.01642","repositories_listed":6,"syntology":null},{"url":"/paper/scalable-object-detection-using-deep-neural","slug":"scalable-object-detection-using-deep-neural","title":"Scalable Object Detection using Deep Neural Networks","date":"2013-12-08","arxiv_id":"1312.2249","repositories_listed":6,"syntology":null},{"url":"/paper/yolov9-learning-what-you-want-to-learn-using","slug":"yolov9-learning-what-you-want-to-learn-using","title":"YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information","date":"2024-02-21","arxiv_id":"2402.13616","repositories_listed":5,"syntology":{"n":32,"n_ran":25,"n_constructed":9,"n_ran_checked":24,"n_instrument":1,"n_unverified":7,"n_honours":2,"n_violates":1,"n_no_contract":21,"n_pointer_only":11,"phrase":"25 ran (of which 9 constructed an object rather than computing a result; 24 with no instrument failure: 2 honoured, 1 violated, 21 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/yolov9-learning-what-you-want-to-learn-using#ran","syntology_url":"https://syntology.ai/paper/2402.13616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13616"}},"official":{"repos":["WongKinYiu/YOLO"],"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":["listed","named_in_paper","official"]}}},{"url":"/paper/retinexformer-one-stage-retinex-based","slug":"retinexformer-one-stage-retinex-based","title":"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement","date":"2023-03-12","arxiv_id":"2303.06705","repositories_listed":5,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"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, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/retinexformer-one-stage-retinex-based#ran","syntology_url":"https://syntology.ai/paper/2303.06705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06705"}},"official":{"repos":["caiyuanhao1998/retinexformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/yolov6-v3-0-a-full-scale-reloading","slug":"yolov6-v3-0-a-full-scale-reloading","title":"YOLOv6 v3.0: A Full-Scale Reloading","date":"2023-01-13","arxiv_id":"2301.05586","repositories_listed":5,"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":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) · 0 unverified","sample_list":"/paper/yolov6-v3-0-a-full-scale-reloading#ran","syntology_url":"https://syntology.ai/paper/2301.05586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.05586"}},"official":{"repos":["meituan/yolov6"],"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/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/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/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/augmenting-convolutional-networks-with","slug":"augmenting-convolutional-networks-with","title":"Augmenting Convolutional networks with attention-based aggregation","date":"2021-12-27","arxiv_id":"2112.13692","repositories_listed":5,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/augmenting-convolutional-networks-with#ran","syntology_url":"https://syntology.ai/paper/2112.13692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13692"}},"official":{"repos":["facebookresearch/deit"],"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/trasw-tracklet-switch-adversarial-attacks","slug":"trasw-tracklet-switch-adversarial-attacks","title":"Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers","date":"2021-11-17","arxiv_id":"2111.08954","repositories_listed":5,"syntology":null},{"url":"/paper/yolop-you-only-look-once-for-panoptic-driving","slug":"yolop-you-only-look-once-for-panoptic-driving","title":"YOLOP: You Only Look Once for Panoptic Driving Perception","date":"2021-08-25","arxiv_id":"2108.11250","repositories_listed":5,"syntology":{"n":18,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"13 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/yolop-you-only-look-once-for-panoptic-driving#ran","syntology_url":"https://syntology.ai/paper/2108.11250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.11250"}},"official":{"repos":["hustvl/yolop"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/oriented-r-cnn-for-object-detection","slug":"oriented-r-cnn-for-object-detection","title":"Oriented R-CNN for Object Detection","date":"2021-08-12","arxiv_id":"2108.05699","repositories_listed":5,"syntology":null},{"url":"/paper/queryinst-parallelly-supervised-mask-query","slug":"queryinst-parallelly-supervised-mask-query","title":"Instances as Queries","date":"2021-05-05","arxiv_id":"2105.01928","repositories_listed":5,"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/queryinst-parallelly-supervised-mask-query#ran","syntology_url":"https://syntology.ai/paper/2105.01928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.01928"}},"official":{"repos":["hustvl/QueryInst"],"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-r-cnn-towards-high-performance-voxel","slug":"voxel-r-cnn-towards-high-performance-voxel","title":"Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection","date":"2020-12-31","arxiv_id":"2012.15712","repositories_listed":5,"syntology":null},{"url":"/paper/simple-copy-paste-is-a-strong-data","slug":"simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","arxiv_id":"2012.07177","repositories_listed":5,"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/simple-copy-paste-is-a-strong-data#ran","syntology_url":"https://syntology.ai/paper/2012.07177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.07177"}},"official":{"repos":["tensorflow/tpu"],"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/generalized-focal-loss-v2-learning-reliable","slug":"generalized-focal-loss-v2-learning-reliable","title":"Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection","date":"2020-11-25","arxiv_id":"2011.12885","repositories_listed":5,"syntology":null},{"url":"/paper/rotate-to-attend-convolutional-triplet","slug":"rotate-to-attend-convolutional-triplet","title":"Rotate to Attend: Convolutional Triplet Attention Module","date":"2020-10-06","arxiv_id":"2010.03045","repositories_listed":5,"syntology":null},{"url":"/paper/asymmetric-loss-for-multi-label","slug":"asymmetric-loss-for-multi-label","title":"Asymmetric Loss For Multi-Label Classification","date":"2020-09-29","arxiv_id":"2009.14119","repositories_listed":5,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"10 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/asymmetric-loss-for-multi-label#ran","syntology_url":"https://syntology.ai/paper/2009.14119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14119"}},"official":{"repos":["Alibaba-MIIL/ASL"],"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/activate-or-not-learning-customized","slug":"activate-or-not-learning-customized","title":"Activate or Not: Learning Customized Activation","date":"2020-09-10","arxiv_id":"2009.04759","repositories_listed":5,"syntology":null},{"url":"/paper/pp-yolo-an-effective-and-efficient","slug":"pp-yolo-an-effective-and-efficient","title":"PP-YOLO: An Effective and Efficient Implementation of Object Detector","date":"2020-07-23","arxiv_id":"2007.12099","repositories_listed":5,"syntology":null},{"url":"/paper/probabilistic-anchor-assignment-with-iou","slug":"probabilistic-anchor-assignment-with-iou","title":"Probabilistic Anchor Assignment with IoU Prediction for Object Detection","date":"2020-07-16","arxiv_id":"2007.08103","repositories_listed":5,"syntology":null},{"url":"/paper/disentangled-non-local-neural-networks","slug":"disentangled-non-local-neural-networks","title":"Disentangled Non-Local Neural Networks","date":"2020-06-11","arxiv_id":"2006.06668","repositories_listed":5,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/disentangled-non-local-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2006.06668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06668"}},"official":{"repos":["Howal/DNL-Object-Detection","yinmh17/DNL-Semantic-Segmentation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-novel-region-of-interest-extraction-layer","slug":"a-novel-region-of-interest-extraction-layer","title":"A novel Region of Interest Extraction Layer for Instance Segmentation","date":"2020-04-28","arxiv_id":"2004.13665","repositories_listed":5,"syntology":null},{"url":"/paper/scrdet-detecting-small-cluttered-and-rotated","slug":"scrdet-detecting-small-cluttered-and-rotated","title":"SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing","date":"2020-04-28","arxiv_id":"2004.13316","repositories_listed":5,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/scrdet-detecting-small-cluttered-and-rotated#ran","syntology_url":"https://syntology.ai/paper/2004.13316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13316"}},"official":{"repos":["SJTU-Thinklab-Det/DOTA-DOAI","Thinklab-SJTU/R3Det_Tensorflow","Thinklab-SJTU/S2TLD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/frustratingly-simple-few-shot-object","slug":"frustratingly-simple-few-shot-object","title":"Frustratingly Simple Few-Shot Object Detection","date":"2020-03-16","arxiv_id":"2003.06957","repositories_listed":5,"syntology":{"n":11,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"5 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; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/frustratingly-simple-few-shot-object#ran","syntology_url":"https://syntology.ai/paper/2003.06957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06957"}},"official":{"repos":["ucbdrive/few-shot-object-detection"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/minneapple-a-benchmark-dataset-for-apple","slug":"minneapple-a-benchmark-dataset-for-apple","title":"MinneApple: A Benchmark Dataset for Apple Detection and Segmentation","date":"2019-09-13","arxiv_id":"1909.06441","repositories_listed":5,"syntology":null},{"url":"/paper/190807906","slug":"190807906","title":"PCRNet: Point Cloud Registration Network using PointNet Encoding","date":"2019-08-21","arxiv_id":"1908.07906","repositories_listed":5,"syntology":null},{"url":"/paper/data-free-quantization-through-weight","slug":"data-free-quantization-through-weight","title":"Data-Free Quantization Through Weight Equalization and Bias Correction","date":"2019-06-11","arxiv_id":"1906.04721","repositories_listed":5,"syntology":null},{"url":"/paper/a-simple-pooling-based-design-for-real-time","slug":"a-simple-pooling-based-design-for-real-time","title":"A Simple Pooling-Based Design for Real-Time Salient Object Detection","date":"2019-04-21","arxiv_id":"1904.09569","repositories_listed":5,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/a-simple-pooling-based-design-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1904.09569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09569"}},"official":null}},{"url":"/paper/precise-detection-in-densely-packed-scenes","slug":"precise-detection-in-densely-packed-scenes","title":"Precise Detection in Densely Packed Scenes","date":"2019-04-01","arxiv_id":"1904.00853","repositories_listed":5,"syntology":null},{"url":"/paper/a-novel-adaptive-learning-rate-scheduler-for","slug":"a-novel-adaptive-learning-rate-scheduler-for","title":"LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence","date":"2019-02-20","arxiv_id":"1902.07399","repositories_listed":5,"syntology":null},{"url":"/paper/augmentation-for-small-object-detection","slug":"augmentation-for-small-object-detection","title":"Augmentation for small object detection","date":"2019-02-19","arxiv_id":"1902.07296","repositories_listed":5,"syntology":null},{"url":"/paper/hybrid-task-cascade-for-instance-segmentation","slug":"hybrid-task-cascade-for-instance-segmentation","title":"Hybrid Task Cascade for Instance Segmentation","date":"2019-01-22","arxiv_id":"1901.07518","repositories_listed":5,"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/hybrid-task-cascade-for-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/1901.07518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07518"}},"official":{"repos":["open-mmlab/mmdetection"],"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/cornernet-detecting-objects-as-paired","slug":"cornernet-detecting-objects-as-paired","title":"CornerNet: Detecting Objects as Paired Keypoints","date":"2018-08-03","arxiv_id":"1808.01244","repositories_listed":5,"syntology":{"n":11,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 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; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/cornernet-detecting-objects-as-paired#ran","syntology_url":"https://syntology.ai/paper/1808.01244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.01244"}},"official":{"repos":["princeton-vl/CornerNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/cubeslam-monocular-3d-object-detection-and","slug":"cubeslam-monocular-3d-object-detection-and","title":"CubeSLAM: Monocular 3D Object SLAM","date":"2018-06-01","arxiv_id":"1806.00557","repositories_listed":5,"syntology":null},{"url":"/paper/object-detection-for-comics-using-manga109","slug":"object-detection-for-comics-using-manga109","title":"Object Detection for Comics using Manga109 Annotations","date":"2018-03-23","arxiv_id":"1803.08670","repositories_listed":5,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/object-detection-for-comics-using-manga109#ran","syntology_url":"https://syntology.ai/paper/1803.08670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08670"}},"official":null}},{"url":"/paper/abnormal-event-detection-in-videos-using","slug":"abnormal-event-detection-in-videos-using","title":"Abnormal Event Detection in Videos using Spatiotemporal Autoencoder","date":"2017-01-06","arxiv_id":"1701.01546","repositories_listed":5,"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/abnormal-event-detection-in-videos-using#ran","syntology_url":"https://syntology.ai/paper/1701.01546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.01546"}},"official":null}},{"url":"/paper/face-detection-with-end-to-end-integration-of","slug":"face-detection-with-end-to-end-integration-of","title":"Face Detection with End-to-End Integration of a ConvNet and a 3D Model","date":"2016-06-02","arxiv_id":"1606.00850","repositories_listed":5,"syntology":null},{"url":"/paper/ternary-weight-networks","slug":"ternary-weight-networks","title":"Ternary Weight Networks","date":"2016-05-16","arxiv_id":"1605.04711","repositories_listed":5,"syntology":null},{"url":"/paper/training-region-based-object-detectors-with","slug":"training-region-based-object-detectors-with","title":"Training Region-based Object Detectors with Online Hard Example Mining","date":"2016-04-12","arxiv_id":"1604.03540","repositories_listed":5,"syntology":null},{"url":"/paper/on-complex-valued-convolutional-neural","slug":"on-complex-valued-convolutional-neural","title":"On Complex Valued Convolutional Neural Networks","date":"2016-02-29","arxiv_id":"1602.09046","repositories_listed":5,"syntology":null},{"url":"/paper/weakly-supervised-deep-detection-networks","slug":"weakly-supervised-deep-detection-networks","title":"Weakly Supervised Deep Detection Networks","date":"2015-11-09","arxiv_id":"1511.02853","repositories_listed":5,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weakly-supervised-deep-detection-networks#ran","syntology_url":"https://syntology.ai/paper/1511.02853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.02853"}},"official":{"repos":["hbilen/WSDDN"],"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/max-margin-object-detection","slug":"max-margin-object-detection","title":"Max-Margin Object Detection","date":"2015-01-31","arxiv_id":"1502.00046","repositories_listed":5,"syntology":null},{"url":"/paper/ntire-2025-challenge-on-cross-domain-few-shot","slug":"ntire-2025-challenge-on-cross-domain-few-shot","title":"NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results","date":"2025-04-14","arxiv_id":"2504.10685","repositories_listed":4,"syntology":null},{"url":"/paper/wholly-wood-wholly-leveraging-diversified","slug":"wholly-wood-wholly-leveraging-diversified","title":"Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection","date":"2025-02-13","arxiv_id":"2502.09471","repositories_listed":4,"syntology":null},{"url":"/paper/mambaout-do-we-really-need-mamba-for-vision","slug":"mambaout-do-we-really-need-mamba-for-vision","title":"MambaOut: Do We Really Need Mamba for Vision?","date":"2024-05-13","arxiv_id":"2405.07992","repositories_listed":4,"syntology":null},{"url":"/paper/transnext-robust-foveal-visual-perception-for","slug":"transnext-robust-foveal-visual-perception-for","title":"TransNeXt: Robust Foveal Visual Perception for Vision Transformers","date":"2023-11-28","arxiv_id":"2311.17132","repositories_listed":4,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/transnext-robust-foveal-visual-perception-for#ran","syntology_url":"https://syntology.ai/paper/2311.17132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17132"}},"official":{"repos":["daishiresearch/transnext"],"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/how-to-evaluate-the-generalization-of","slug":"how-to-evaluate-the-generalization-of","title":"How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection","date":"2023-08-25","arxiv_id":"2308.13177","repositories_listed":4,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-to-evaluate-the-generalization-of#ran","syntology_url":"https://syntology.ai/paper/2308.13177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13177"}},"official":{"repos":["om-ai-lab/ovdeval"],"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/hiera-a-hierarchical-vision-transformer","slug":"hiera-a-hierarchical-vision-transformer","title":"Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles","date":"2023-06-01","arxiv_id":"2306.00989","repositories_listed":4,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"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, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hiera-a-hierarchical-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2306.00989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00989"}},"official":{"repos":["facebookresearch/hiera"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/octformer-octree-based-transformers-for-3d","slug":"octformer-octree-based-transformers-for-3d","title":"OctFormer: Octree-based Transformers for 3D Point Clouds","date":"2023-05-04","arxiv_id":"2305.03045","repositories_listed":4,"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/octformer-octree-based-transformers-for-3d#ran","syntology_url":"https://syntology.ai/paper/2305.03045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03045"}},"official":{"repos":["octree-nn/octformer"],"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":["listed","official"]}}},{"url":"/paper/a-closer-look-at-the-training-dynamics-of","slug":"a-closer-look-at-the-training-dynamics-of","title":"Understanding the Role of the Projector in Knowledge Distillation","date":"2023-03-20","arxiv_id":"2303.11098","repositories_listed":4,"syntology":null},{"url":"/paper/efficient-teacher-semi-supervised-object","slug":"efficient-teacher-semi-supervised-object","title":"Efficient Teacher: Semi-Supervised Object Detection for YOLOv5","date":"2023-02-15","arxiv_id":"2302.07577","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-teacher-semi-supervised-object#ran","syntology_url":"https://syntology.ai/paper/2302.07577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07577"}},"official":{"repos":["AlibabaResearch/efficientteacher"],"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/reversible-vision-transformers-1","slug":"reversible-vision-transformers-1","title":"Reversible Vision Transformers","date":"2023-02-09","arxiv_id":"2302.04869","repositories_listed":4,"syntology":{"n":29,"n_ran":24,"n_constructed":5,"n_ran_checked":23,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":23,"n_pointer_only":26,"phrase":"24 ran (of which 5 constructed an object rather than computing a result; 23 with no instrument failure: 0 honoured, 0 violated, 23 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/reversible-vision-transformers-1#ran","syntology_url":"https://syntology.ai/paper/2302.04869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04869"}},"official":{"repos":["karttikeya/minrev","facebookresearch/SlowFast","facebookresearch/mvit"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":5,"n_ran_no_instrument_failure":21,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/dsvt-dynamic-sparse-voxel-transformer-with","slug":"dsvt-dynamic-sparse-voxel-transformer-with","title":"DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets","date":"2023-01-15","arxiv_id":"2301.06051","repositories_listed":4,"syntology":null},{"url":"/paper/expediting-large-scale-vision-transformer-for","slug":"expediting-large-scale-vision-transformer-for","title":"Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning","date":"2022-10-03","arxiv_id":"2210.01035","repositories_listed":4,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/expediting-large-scale-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2210.01035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01035"}},"official":{"repos":["Expedit-LargeScale-Vision-Transformer/Expedit-DINO","Expedit-LargeScale-Vision-Transformer/Expedit-DPT","Expedit-LargeScale-Vision-Transformer/Expedit-Segmenter"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ssfpn-scale-sequence-s-2-feature-based","slug":"ssfpn-scale-sequence-s-2-feature-based","title":"ssFPN: Scale Sequence (S^2) Feature Based-Feature Pyramid Network for Object Detection","date":"2022-08-24","arxiv_id":"2208.11533","repositories_listed":4,"syntology":null},{"url":"/paper/fully-sparse-3d-object-detection","slug":"fully-sparse-3d-object-detection","title":"Fully Sparse 3D Object Detection","date":"2022-07-20","arxiv_id":"2207.10035","repositories_listed":4,"syntology":null},{"url":"/paper/doclaynet-a-large-human-annotated-dataset-for","slug":"doclaynet-a-large-human-annotated-dataset-for","title":"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis","date":"2022-06-02","arxiv_id":"2206.01062","repositories_listed":4,"syntology":null},{"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/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/improving-object-detection-multi-object","slug":"improving-object-detection-multi-object","title":"Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones","date":"2022-01-05","arxiv_id":"2201.01494","repositories_listed":4,"syntology":null},{"url":"/paper/pp-picodet-a-better-real-time-object-detector","slug":"pp-picodet-a-better-real-time-object-detector","title":"PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices","date":"2021-11-01","arxiv_id":"2111.00902","repositories_listed":4,"syntology":null},{"url":"/paper/non-deep-networks-1","slug":"non-deep-networks-1","title":"Non-deep Networks","date":"2021-10-14","arxiv_id":"2110.07641","repositories_listed":4,"syntology":{"n":18,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/non-deep-networks-1#ran","syntology_url":"https://syntology.ai/paper/2110.07641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07641"}},"official":{"repos":["imankgoyal/NonDeepNetworks"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/convmlp-hierarchical-convolutional-mlps-for","slug":"convmlp-hierarchical-convolutional-mlps-for","title":"ConvMLP: Hierarchical Convolutional MLPs for Vision","date":"2021-09-09","arxiv_id":"2109.04454","repositories_listed":4,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/convmlp-hierarchical-convolutional-mlps-for#ran","syntology_url":"https://syntology.ai/paper/2109.04454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04454"}},"official":{"repos":["SHI-Labs/Convolutional-MLPs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/improving-object-detection-by-label","slug":"improving-object-detection-by-label","title":"Improving Object Detection by Label Assignment Distillation","date":"2021-08-24","arxiv_id":"2108.10520","repositories_listed":4,"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/improving-object-detection-by-label#ran","syntology_url":"https://syntology.ai/paper/2108.10520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.10520"}},"official":{"repos":["cybercore-co-ltd/CoLAD","cybercore-co-ltd/colad_paper","open-mmlab/mmdetection"],"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/conditional-detr-for-fast-training","slug":"conditional-detr-for-fast-training","title":"Conditional DETR for Fast Training Convergence","date":"2021-08-13","arxiv_id":"2108.06152","repositories_listed":4,"syntology":{"n":9,"n_ran":6,"n_constructed":1,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":4,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/conditional-detr-for-fast-training#ran","syntology_url":"https://syntology.ai/paper/2108.06152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06152"}},"official":{"repos":["atten4vis/conditionaldetr"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mobile-former-bridging-mobilenet-and","slug":"mobile-former-bridging-mobilenet-and","title":"Mobile-Former: Bridging MobileNet and Transformer","date":"2021-08-12","arxiv_id":"2108.05895","repositories_listed":4,"syntology":null},{"url":"/paper/crossformer-a-versatile-vision-transformer","slug":"crossformer-a-versatile-vision-transformer","title":"CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention","date":"2021-07-31","arxiv_id":"2108.00154","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/crossformer-a-versatile-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2108.00154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00154"}},"official":{"repos":["cheerss/CrossFormer"],"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/cbnetv2-a-composite-backbone-network","slug":"cbnetv2-a-composite-backbone-network","title":"CBNet: A Composite Backbone Network Architecture for Object Detection","date":"2021-07-01","arxiv_id":"2107.00420","repositories_listed":4,"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/cbnetv2-a-composite-backbone-network#ran","syntology_url":"https://syntology.ai/paper/2107.00420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00420"}},"official":{"repos":["VDIGPKU/CBNetV2"],"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/p2t-pyramid-pooling-transformer-for-scene","slug":"p2t-pyramid-pooling-transformer-for-scene","title":"P2T: Pyramid Pooling Transformer for Scene Understanding","date":"2021-06-22","arxiv_id":"2106.12011","repositories_listed":4,"syntology":null},{"url":"/paper/structured-sparse-r-cnn-for-direct-scene","slug":"structured-sparse-r-cnn-for-direct-scene","title":"Structured Sparse R-CNN for Direct Scene Graph Generation","date":"2021-06-21","arxiv_id":"2106.10815","repositories_listed":4,"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/structured-sparse-r-cnn-for-direct-scene#ran","syntology_url":"https://syntology.ai/paper/2106.10815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10815"}},"official":{"repos":["mcg-nju/structured-sparse-rcnn"],"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/shuffle-transformer-rethinking-spatial","slug":"shuffle-transformer-rethinking-spatial","title":"Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer","date":"2021-06-07","arxiv_id":"2106.03650","repositories_listed":4,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":6,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/shuffle-transformer-rethinking-spatial#ran","syntology_url":"https://syntology.ai/paper/2106.03650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03650"}},"official":null}},{"url":"/paper/regionvit-regional-to-local-attention-for","slug":"regionvit-regional-to-local-attention-for","title":"RegionViT: Regional-to-Local Attention for Vision Transformers","date":"2021-06-04","arxiv_id":"2106.02689","repositories_listed":4,"syntology":{"n":25,"n_ran":13,"n_constructed":9,"n_ran_checked":11,"n_instrument":2,"n_unverified":12,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"13 ran (of which 9 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/regionvit-regional-to-local-attention-for#ran","syntology_url":"https://syntology.ai/paper/2106.02689","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02689"}},"official":{"repos":["IBM/RegionViT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/container-context-aggregation-network","slug":"container-context-aggregation-network","title":"Container: Context Aggregation Network","date":"2021-06-02","arxiv_id":"2106.01401","repositories_listed":4,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/container-context-aggregation-network#ran","syntology_url":"https://syntology.ai/paper/2106.01401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01401"}},"official":{"repos":["allenai/container","gaopengcuhk/Container"],"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/epsanet-an-efficient-pyramid-split-attention","slug":"epsanet-an-efficient-pyramid-split-attention","title":"EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network","date":"2021-05-30","arxiv_id":"2105.14447","repositories_listed":4,"syntology":null},{"url":"/paper/conformer-local-features-coupling-global","slug":"conformer-local-features-coupling-global","title":"Conformer: Local Features Coupling Global Representations for Visual Recognition","date":"2021-05-09","arxiv_id":"2105.03889","repositories_listed":4,"syntology":{"n":12,"n_ran":7,"n_constructed":5,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 5 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) · 5 unverified","sample_list":"/paper/conformer-local-features-coupling-global#ran","syntology_url":"https://syntology.ai/paper/2105.03889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.03889"}},"official":{"repos":["pengzhiliang/Conformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/zero-shot-detection-via-vision-and-language","slug":"zero-shot-detection-via-vision-and-language","title":"Open-vocabulary Object Detection via Vision and Language Knowledge Distillation","date":"2021-04-28","arxiv_id":"2104.13921","repositories_listed":4,"syntology":null},{"url":"/paper/zero-shot-instance-segmentation","slug":"zero-shot-instance-segmentation","title":"Zero-Shot Instance Segmentation","date":"2021-04-14","arxiv_id":"2104.06601","repositories_listed":4,"syntology":null},{"url":"/paper/group-free-3d-object-detection-via","slug":"group-free-3d-object-detection-via","title":"Group-Free 3D Object Detection via Transformers","date":"2021-04-01","arxiv_id":"2104.00678","repositories_listed":4,"syntology":{"n":17,"n_ran":9,"n_constructed":7,"n_ran_checked":8,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/group-free-3d-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2104.00678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00678"}},"official":{"repos":["zeliu98/Group-Free-3D"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/multi-view-radar-semantic-segmentation","slug":"multi-view-radar-semantic-segmentation","title":"Multi-View Radar Semantic Segmentation","date":"2021-03-30","arxiv_id":"2103.16214","repositories_listed":4,"syntology":{"n":13,"n_ran":9,"n_constructed":7,"n_ran_checked":7,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":13,"phrase":"9 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/multi-view-radar-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.16214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16214"}},"official":{"repos":["valeoai/MVRSS"],"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/redet-a-rotation-equivariant-detector-for","slug":"redet-a-rotation-equivariant-detector-for","title":"ReDet: A Rotation-equivariant Detector for Aerial Object Detection","date":"2021-03-13","arxiv_id":"2103.07733","repositories_listed":4,"syntology":null},{"url":"/paper/beyond-max-margin-class-margin-equilibrium","slug":"beyond-max-margin-class-margin-equilibrium","title":"Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection","date":"2021-03-08","arxiv_id":"2103.04612","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"3 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/beyond-max-margin-class-margin-equilibrium#ran","syntology_url":"https://syntology.ai/paper/2103.04612","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04612"}},"official":{"repos":["Bohao-Lee/CME"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unbiased-teacher-for-semi-supervised-object-1","slug":"unbiased-teacher-for-semi-supervised-object-1","title":"Unbiased Teacher for Semi-Supervised Object Detection","date":"2021-02-18","arxiv_id":"2102.09480","repositories_listed":4,"syntology":null},{"url":"/paper/hardnet-mseg-a-simple-encoder-decoder-polyp","slug":"hardnet-mseg-a-simple-encoder-decoder-polyp","title":"HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS","date":"2021-01-18","arxiv_id":"2101.07172","repositories_listed":4,"syntology":null}],"record_sha256":"7b341d8588a368e3296544d1ecef6c434f5437d66fcfd08bd4e4659b9d86da15","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}