{"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/9","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":9,"pages_in_order":12,"rows_per_page":100,"rows":[801,900],"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/8","next":"/task/object-detection/papers/ran/10","papers":[{"url":"/paper/distribution-alignment-a-unified-framework","slug":"distribution-alignment-a-unified-framework","title":"Distribution Alignment: A Unified Framework for Long-tail Visual Recognition","date":"2021-03-30","arxiv_id":"2103.16370","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/distribution-alignment-a-unified-framework#ran","syntology_url":"https://syntology.ai/paper/2103.16370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16370"}},"official":{"repos":["Megvii-BaseDetection/DisAlign"],"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/depth-conditioned-dynamic-message-propagation","slug":"depth-conditioned-dynamic-message-propagation","title":"Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection","date":"2021-03-30","arxiv_id":"2103.16470","repositories_listed":1,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/depth-conditioned-dynamic-message-propagation#ran","syntology_url":"https://syntology.ai/paper/2103.16470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16470"}},"official":{"repos":["fudan-zvg/DDMP"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-relation-distillation-with-context","slug":"dense-relation-distillation-with-context","title":"Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection","date":"2021-03-30","arxiv_id":"2103.17115","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/dense-relation-distillation-with-context#ran","syntology_url":"https://syntology.ai/paper/2103.17115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17115"}},"official":{"repos":["hzhupku/DCNet"],"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":["official"]}}},{"url":"/paper/fooling-lidar-perception-via-adversarial","slug":"fooling-lidar-perception-via-adversarial","title":"Fooling LiDAR Perception via Adversarial Trajectory Perturbation","date":"2021-03-29","arxiv_id":"2103.15326","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":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fooling-lidar-perception-via-adversarial#ran","syntology_url":"https://syntology.ai/paper/2103.15326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15326"}},"official":null}},{"url":"/paper/2103-15358","slug":"2103-15358","title":"Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding","date":"2021-03-29","arxiv_id":"2103.15358","repositories_listed":3,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":2,"n_no_contract":2,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/2103-15358#ran","syntology_url":"https://syntology.ai/paper/2103.15358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15358"}},"official":{"repos":["microsoft/vision-longformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/generic-attention-model-explainability-for","slug":"generic-attention-model-explainability-for","title":"Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers","date":"2021-03-29","arxiv_id":"2103.15679","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/generic-attention-model-explainability-for#ran","syntology_url":"https://syntology.ai/paper/2103.15679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15679"}},"official":{"repos":["hila-chefer/Transformer-MM-Explainability"],"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-to-track-with-object-permanence","slug":"learning-to-track-with-object-permanence","title":"Learning to Track with Object Permanence","date":"2021-03-26","arxiv_id":"2103.14258","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-to-track-with-object-permanence#ran","syntology_url":"https://syntology.ai/paper/2103.14258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14258"}},"official":{"repos":["TRI-ML/permatrack"],"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":["official"]}}},{"url":"/paper/ota-optimal-transport-assignment-for-object","slug":"ota-optimal-transport-assignment-for-object","title":"OTA: Optimal Transport Assignment for Object Detection","date":"2021-03-26","arxiv_id":"2103.14259","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"3 ran (of which 1 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/ota-optimal-transport-assignment-for-object#ran","syntology_url":"https://syntology.ai/paper/2103.14259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14259"}},"official":{"repos":["Megvii-BaseDetection/OTA"],"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":["listed","official"]}}},{"url":"/paper/metaalign-coordinating-domain-alignment-and","slug":"metaalign-coordinating-domain-alignment-and","title":"MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation","date":"2021-03-25","arxiv_id":"2103.13575","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 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; 3 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/metaalign-coordinating-domain-alignment-and#ran","syntology_url":"https://syntology.ai/paper/2103.13575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13575"}},"official":null}},{"url":"/paper/swin-transformer-hierarchical-vision","slug":"swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","arxiv_id":"2103.14030","repositories_listed":80,"syntology":{"n":207,"n_ran":123,"n_constructed":45,"n_ran_checked":82,"n_instrument":41,"n_unverified":84,"n_honours":5,"n_violates":2,"n_no_contract":75,"n_pointer_only":45,"phrase":"123 ran (of which 45 constructed an object rather than computing a result; 82 with no instrument failure: 5 honoured, 2 violated, 75 with no contract checked; 41 where Syntology's instrument failed) · 84 unverified","sample_list":"/paper/swin-transformer-hierarchical-vision#ran","syntology_url":"https://syntology.ai/paper/2103.14030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14030"}},"official":{"repos":["microsoft/Swin-Transformer"],"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/region-similarity-representation-learning","slug":"region-similarity-representation-learning","title":"Region Similarity Representation Learning","date":"2021-03-24","arxiv_id":"2103.12902","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/region-similarity-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2103.12902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12902"}},"official":{"repos":["Tete-Xiao/ReSim"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/m3dssd-monocular-3d-single-stage-object","slug":"m3dssd-monocular-3d-single-stage-object","title":"M3DSSD: Monocular 3D Single Stage Object Detector","date":"2021-03-24","arxiv_id":"2103.13164","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/m3dssd-monocular-3d-single-stage-object#ran","syntology_url":"https://syntology.ai/paper/2103.13164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13164"}},"official":{"repos":["mumianyuxin/M3DSSD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diverse-branch-block-building-a-convolution","slug":"diverse-branch-block-building-a-convolution","title":"Diverse Branch Block: Building a Convolution as an Inception-like Unit","date":"2021-03-24","arxiv_id":"2103.13425","repositories_listed":3,"syntology":{"n":20,"n_ran":11,"n_constructed":3,"n_ran_checked":4,"n_instrument":7,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"11 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/diverse-branch-block-building-a-convolution#ran","syntology_url":"https://syntology.ai/paper/2103.13425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13425"}},"official":{"repos":["DingXiaoH/DiverseBranchBlock"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/a-broad-study-on-the-transferability-of","slug":"a-broad-study-on-the-transferability-of","title":"A Broad Study on the Transferability of Visual Representations with Contrastive Learning","date":"2021-03-24","arxiv_id":"2103.13517","repositories_listed":2,"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/a-broad-study-on-the-transferability-of#ran","syntology_url":"https://syntology.ai/paper/2103.13517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13517"}},"official":{"repos":["asrafulashiq/transfer_broad"],"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/deep-occlusion-aware-instance-segmentation","slug":"deep-occlusion-aware-instance-segmentation","title":"Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers","date":"2021-03-23","arxiv_id":"2103.12340","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":5,"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/deep-occlusion-aware-instance-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.12340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12340"}},"official":{"repos":["lkeab/BCNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/monorun-monocular-3d-object-detection-by-self","slug":"monorun-monocular-3d-object-detection-by-self","title":"MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation","date":"2021-03-23","arxiv_id":"2103.12605","repositories_listed":1,"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/monorun-monocular-3d-object-detection-by-self#ran","syntology_url":"https://syntology.ai/paper/2103.12605","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12605"}},"official":{"repos":["tjiiv-cprg/MonoRUn"],"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/scaling-local-self-attention-for-parameter","slug":"scaling-local-self-attention-for-parameter","title":"Scaling Local Self-Attention for Parameter Efficient Visual Backbones","date":"2021-03-23","arxiv_id":"2103.12731","repositories_listed":7,"syntology":{"n":20,"n_ran":18,"n_constructed":4,"n_ran_checked":11,"n_instrument":7,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"18 ran (of which 4 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scaling-local-self-attention-for-parameter#ran","syntology_url":"https://syntology.ai/paper/2103.12731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12731"}},"official":null}},{"url":"/paper/meta-detr-few-shot-object-detection-via","slug":"meta-detr-few-shot-object-detection-via","title":"Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation","date":"2021-03-22","arxiv_id":"2103.11731","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/meta-detr-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.11731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11731"}},"official":{"repos":["ZhangGongjie/Meta-DETR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-trainable-multi-instance-pose","slug":"end-to-end-trainable-multi-instance-pose","title":"End-to-End Trainable Multi-Instance Pose Estimation with Transformers","date":"2021-03-22","arxiv_id":"2103.12115","repositories_listed":2,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":3,"n_honours":4,"n_violates":1,"n_no_contract":8,"n_pointer_only":8,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 4 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/end-to-end-trainable-multi-instance-pose#ran","syntology_url":"https://syntology.ai/paper/2103.12115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12115"}},"official":{"repos":["amathislab/poet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/3d-human-pose-estimation-with-spatial-and","slug":"3d-human-pose-estimation-with-spatial-and","title":"3D Human Pose Estimation with Spatial and Temporal Transformers","date":"2021-03-18","arxiv_id":"2103.10455","repositories_listed":3,"syntology":{"n":8,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"4 ran (of which 2 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/3d-human-pose-estimation-with-spatial-and#ran","syntology_url":"https://syntology.ai/paper/2103.10455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.10455"}},"official":{"repos":["zczcwh/PoseFormer"],"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":["listed","official"]}}},{"url":"/paper/track-to-detect-and-segment-an-online-multi","slug":"track-to-detect-and-segment-an-online-multi","title":"Track to Detect and Segment: An Online Multi-Object Tracker","date":"2021-03-16","arxiv_id":"2103.08808","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/track-to-detect-and-segment-an-online-multi#ran","syntology_url":"https://syntology.ai/paper/2103.08808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08808"}},"official":{"repos":["JialianW/TraDeS"],"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/refine-myself-by-teaching-myself-feature","slug":"refine-myself-by-teaching-myself-feature","title":"Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation","date":"2021-03-15","arxiv_id":"2103.08273","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/refine-myself-by-teaching-myself-feature#ran","syntology_url":"https://syntology.ai/paper/2103.08273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08273"}},"official":{"repos":["MingiJi/FRSKD"],"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/fsce-few-shot-object-detection-via","slug":"fsce-few-shot-object-detection-via","title":"FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding","date":"2021-03-10","arxiv_id":"2103.05950","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":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) · 0 unverified","sample_list":"/paper/fsce-few-shot-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.05950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05950"}},"official":{"repos":["MegviiDetection/FSCE"],"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/split-computing-and-early-exiting-for-deep","slug":"split-computing-and-early-exiting-for-deep","title":"Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges","date":"2021-03-08","arxiv_id":"2103.04505","repositories_listed":19,"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":2,"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/split-computing-and-early-exiting-for-deep#ran","syntology_url":"https://syntology.ai/paper/2103.04505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04505"}},"official":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/barlow-twins-self-supervised-learning-via","slug":"barlow-twins-self-supervised-learning-via","title":"Barlow Twins: Self-Supervised Learning via Redundancy Reduction","date":"2021-03-04","arxiv_id":"2103.03230","repositories_listed":24,"syntology":{"n":26,"n_ran":21,"n_constructed":6,"n_ran_checked":15,"n_instrument":6,"n_unverified":5,"n_honours":2,"n_violates":1,"n_no_contract":12,"n_pointer_only":10,"phrase":"21 ran (of which 6 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 1 violated, 12 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/barlow-twins-self-supervised-learning-via#ran","syntology_url":"https://syntology.ai/paper/2103.03230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03230"}},"official":{"repos":["facebookresearch/barlowtwins"],"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":["community","listed","official"]}}},{"url":"/paper/categorical-depth-distribution-network-for","slug":"categorical-depth-distribution-network-for","title":"Categorical Depth Distribution Network for Monocular 3D Object Detection","date":"2021-03-01","arxiv_id":"2103.01100","repositories_listed":2,"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":0,"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/categorical-depth-distribution-network-for#ran","syntology_url":"https://syntology.ai/paper/2103.01100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01100"}},"official":{"repos":["TRAILab/CaDDN"],"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/pyramid-vision-transformer-a-versatile","slug":"pyramid-vision-transformer-a-versatile","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","date":"2021-02-24","arxiv_id":"2102.12122","repositories_listed":11,"syntology":{"n":30,"n_ran":22,"n_constructed":16,"n_ran_checked":18,"n_instrument":4,"n_unverified":8,"n_honours":2,"n_violates":0,"n_no_contract":16,"n_pointer_only":1,"phrase":"22 ran (of which 16 constructed an object rather than computing a result; 18 with no instrument failure: 2 honoured, 0 violated, 16 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/pyramid-vision-transformer-a-versatile#ran","syntology_url":"https://syntology.ai/paper/2102.12122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12122"}},"official":{"repos":["whai362/PVT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/concealed-object-detection","slug":"concealed-object-detection","title":"Concealed Object Detection","date":"2021-02-20","arxiv_id":"2102.10274","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/concealed-object-detection#ran","syntology_url":"https://syntology.ai/paper/2102.10274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10274"}},"official":{"repos":["GewelsJI/SINet-V2"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/center-smoothing-for-certifiably-robust","slug":"center-smoothing-for-certifiably-robust","title":"Center Smoothing: Certified Robustness for Networks with Structured Outputs","date":"2021-02-19","arxiv_id":"2102.09701","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 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/center-smoothing-for-certifiably-robust#ran","syntology_url":"https://syntology.ai/paper/2102.09701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.09701"}},"official":{"repos":["aounon/center-smoothing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/combining-events-and-frames-using-recurrent","slug":"combining-events-and-frames-using-recurrent","title":"Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction","date":"2021-02-18","arxiv_id":"2102.09320","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":3,"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/combining-events-and-frames-using-recurrent#ran","syntology_url":"https://syntology.ai/paper/2102.09320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.09320"}},"official":{"repos":["uzh-rpg/rpg_ramnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lambdanetworks-modeling-long-range-1","slug":"lambdanetworks-modeling-long-range-1","title":"LambdaNetworks: Modeling Long-Range Interactions Without Attention","date":"2021-02-17","arxiv_id":"2102.08602","repositories_listed":7,"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":2,"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/lambdanetworks-modeling-long-range-1#ran","syntology_url":"https://syntology.ai/paper/2102.08602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08602"}},"official":null}},{"url":"/paper/a-simple-and-effective-use-of-object-centric","slug":"a-simple-and-effective-use-of-object-centric","title":"MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection","date":"2021-02-17","arxiv_id":"2102.08884","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/a-simple-and-effective-use-of-object-centric#ran","syntology_url":"https://syntology.ai/paper/2102.08884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08884"}},"official":{"repos":["czhang0528/MosaicOS"],"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/brecq-pushing-the-limit-of-post-training-1","slug":"brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","arxiv_id":"2102.05426","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/brecq-pushing-the-limit-of-post-training-1#ran","syntology_url":"https://syntology.ai/paper/2102.05426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.05426"}},"official":{"repos":["yhhhli/BRECQ"],"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/negative-data-augmentation-1","slug":"negative-data-augmentation-1","title":"Negative Data Augmentation","date":"2021-02-09","arxiv_id":"2102.05113","repositories_listed":2,"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/negative-data-augmentation-1#ran","syntology_url":"https://syntology.ai/paper/2102.05113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.05113"}},"official":{"repos":["ermongroup/NDA"],"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/sa-net-shuffle-attention-for-deep","slug":"sa-net-shuffle-attention-for-deep","title":"SA-Net: Shuffle Attention for Deep Convolutional Neural Networks","date":"2021-01-30","arxiv_id":"2102.00240","repositories_listed":3,"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":2,"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/sa-net-shuffle-attention-for-deep#ran","syntology_url":"https://syntology.ai/paper/2102.00240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.00240"}},"official":{"repos":["wofmanaf/SA-Net"],"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/bottleneck-transformers-for-visual","slug":"bottleneck-transformers-for-visual","title":"Bottleneck Transformers for Visual Recognition","date":"2021-01-27","arxiv_id":"2101.11605","repositories_listed":13,"syntology":{"n":49,"n_ran":26,"n_constructed":9,"n_ran_checked":19,"n_instrument":7,"n_unverified":23,"n_honours":1,"n_violates":0,"n_no_contract":18,"n_pointer_only":8,"phrase":"26 ran (of which 9 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 7 where Syntology's instrument failed) · 23 unverified","sample_list":"/paper/bottleneck-transformers-for-visual#ran","syntology_url":"https://syntology.ai/paper/2101.11605","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.11605"}},"official":null}},{"url":"/paper/rgb-d-salient-object-detection-via-3d","slug":"rgb-d-salient-object-detection-via-3d","title":"RGB-D Salient Object Detection via 3D Convolutional Neural Networks","date":"2021-01-25","arxiv_id":"2101.10241","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":4,"n_ran_checked":5,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"9 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rgb-d-salient-object-detection-via-3d#ran","syntology_url":"https://syntology.ai/paper/2101.10241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.10241"}},"official":{"repos":["PPOLYpubki/RD3D"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/estimating-and-evaluating-regression","slug":"estimating-and-evaluating-regression","title":"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors","date":"2021-01-13","arxiv_id":"2101.05036","repositories_listed":3,"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/estimating-and-evaluating-regression#ran","syntology_url":"https://syntology.ai/paper/2101.05036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.05036"}},"official":{"repos":["asharakeh/probdet"],"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/self-supervised-pretraining-of-3d-features-on","slug":"self-supervised-pretraining-of-3d-features-on","title":"Self-Supervised Pretraining of 3D Features on any Point-Cloud","date":"2021-01-07","arxiv_id":"2101.02691","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/self-supervised-pretraining-of-3d-features-on#ran","syntology_url":"https://syntology.ai/paper/2101.02691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02691"}},"official":{"repos":["facebookresearch/DepthContrast"],"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":["official"]}}},{"url":"/paper/vinvl-making-visual-representations-matter-in","slug":"vinvl-making-visual-representations-matter-in","title":"VinVL: Revisiting Visual Representations in Vision-Language Models","date":"2021-01-02","arxiv_id":"2101.00529","repositories_listed":7,"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":1,"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/vinvl-making-visual-representations-matter-in#ran","syntology_url":"https://syntology.ai/paper/2101.00529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.00529"}},"official":{"repos":["pzzhang/VinVL"],"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/online-bag-of-visual-words-generation-for","slug":"online-bag-of-visual-words-generation-for","title":"OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning","date":"2020-12-21","arxiv_id":"2012.11552","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/online-bag-of-visual-words-generation-for#ran","syntology_url":"https://syntology.ai/paper/2012.11552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.11552"}},"official":{"repos":["valeoai/obow"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"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/how-well-do-self-supervised-models-transfer","slug":"how-well-do-self-supervised-models-transfer","title":"How Well Do Self-Supervised Models Transfer?","date":"2020-11-26","arxiv_id":"2011.13377","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/how-well-do-self-supervised-models-transfer#ran","syntology_url":"https://syntology.ai/paper/2011.13377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13377"}},"official":{"repos":["linusericsson/ssl-transfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/torchdistill-a-modular-configuration-driven","slug":"torchdistill-a-modular-configuration-driven","title":"torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation","date":"2020-11-25","arxiv_id":"2011.12913","repositories_listed":1,"syntology":{"n":26,"n_ran":10,"n_constructed":0,"n_ran_checked":0,"n_instrument":10,"n_unverified":16,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"10 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; 10 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/torchdistill-a-modular-configuration-driven#ran","syntology_url":"https://syntology.ai/paper/2011.12913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12913"}},"official":{"repos":["yoshitomo-matsubara/torchdistill"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":16,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-transformer-based-set-prediction","slug":"rethinking-transformer-based-set-prediction","title":"Rethinking Transformer-based Set Prediction for Object Detection","date":"2020-11-21","arxiv_id":"2011.10881","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rethinking-transformer-based-set-prediction#ran","syntology_url":"https://syntology.ai/paper/2011.10881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10881"}},"official":{"repos":["edward-sun/tsp-detection"],"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/a-review-and-comparative-study-on","slug":"a-review-and-comparative-study-on","title":"A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving","date":"2020-11-20","arxiv_id":"2011.10671","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/a-review-and-comparative-study-on#ran","syntology_url":"https://syntology.ai/paper/2011.10671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10671"}},"official":{"repos":["asharakeh/pod_compare"],"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/geography-aware-self-supervised-learning","slug":"geography-aware-self-supervised-learning","title":"Geography-Aware Self-Supervised Learning","date":"2020-11-19","arxiv_id":"2011.09980","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/geography-aware-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2011.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.09980"}},"official":{"repos":["sustainlab-group/geography-aware-ssl"],"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/propagate-yourself-exploring-pixel-level","slug":"propagate-yourself-exploring-pixel-level","title":"Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning","date":"2020-11-19","arxiv_id":"2011.10043","repositories_listed":7,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/propagate-yourself-exploring-pixel-level#ran","syntology_url":"https://syntology.ai/paper/2011.10043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10043"}},"official":{"repos":["zdaxie/PixPro"],"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/dense-contrastive-learning-for-self","slug":"dense-contrastive-learning-for-self","title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training","date":"2020-11-18","arxiv_id":"2011.09157","repositories_listed":7,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dense-contrastive-learning-for-self#ran","syntology_url":"https://syntology.ai/paper/2011.09157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.09157"}},"official":null}},{"url":"/paper/scaled-yolov4-scaling-cross-stage-partial","slug":"scaled-yolov4-scaling-cross-stage-partial","title":"Scaled-YOLOv4: Scaling Cross Stage Partial Network","date":"2020-11-16","arxiv_id":"2011.08036","repositories_listed":41,"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/scaled-yolov4-scaling-cross-stage-partial#ran","syntology_url":"https://syntology.ai/paper/2011.08036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.08036"}},"official":{"repos":["WongKinYiu/ScaledYOLOv4"],"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/towards-efficient-scene-understanding-via","slug":"towards-efficient-scene-understanding-via","title":"Towards Efficient Scene Understanding via Squeeze Reasoning","date":"2020-11-06","arxiv_id":"2011.03308","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":2,"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/towards-efficient-scene-understanding-via#ran","syntology_url":"https://syntology.ai/paper/2011.03308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.03308"}},"official":{"repos":["lxtGH/SFSegNets"],"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","unlocated"]}}},{"url":"/paper/faraway-frustum-dealing-with-lidar-sparsity","slug":"faraway-frustum-dealing-with-lidar-sparsity","title":"Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion","date":"2020-11-03","arxiv_id":"2011.01404","repositories_listed":1,"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":4,"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/faraway-frustum-dealing-with-lidar-sparsity#ran","syntology_url":"https://syntology.ai/paper/2011.01404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01404"}},"official":{"repos":["dongfang-steven-yang/faraway-frustum"],"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/in-defense-of-feature-mimicking-for-knowledge","slug":"in-defense-of-feature-mimicking-for-knowledge","title":"Distilling Knowledge by Mimicking Features","date":"2020-11-03","arxiv_id":"2011.01424","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/in-defense-of-feature-mimicking-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2011.01424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01424"}},"official":{"repos":["DoctorKey/LSHFM.detection","DoctorKey/LSHFM.multiclassification","DoctorKey/LSHFM.singleclassification"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/cream-of-the-crop-distilling-prioritized","slug":"cream-of-the-crop-distilling-prioritized","title":"Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search","date":"2020-10-29","arxiv_id":"2010.15821","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/cream-of-the-crop-distilling-prioritized#ran","syntology_url":"https://syntology.ai/paper/2010.15821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15821"}},"official":{"repos":["microsoft/cream"],"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/relationnet-bridging-visual-representations","slug":"relationnet-bridging-visual-representations","title":"RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder","date":"2020-10-29","arxiv_id":"2010.15831","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/relationnet-bridging-visual-representations#ran","syntology_url":"https://syntology.ai/paper/2010.15831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15831"}},"official":{"repos":["microsoft/RelationNet2"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gan-mask-r-cnn-instance-semantic-segmentation","slug":"gan-mask-r-cnn-instance-semantic-segmentation","title":"Instance Semantic Segmentation Benefits from Generative Adversarial Networks","date":"2020-10-26","arxiv_id":"2010.13757","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/gan-mask-r-cnn-instance-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2010.13757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13757"}},"official":{"repos":["quangle2110/GAN_Mask-RCNN"],"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/comprehensive-attention-self-distillation-for","slug":"comprehensive-attention-self-distillation-for","title":"Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection","date":"2020-10-22","arxiv_id":"2010.12023","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/comprehensive-attention-self-distillation-for#ran","syntology_url":"https://syntology.ai/paper/2010.12023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12023"}},"official":{"repos":["DeLightCMU/CASD"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-efficacy-of-neural-planning-metrics-a","slug":"the-efficacy-of-neural-planning-metrics-a","title":"The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes","date":"2020-10-19","arxiv_id":"2010.09350","repositories_listed":1,"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/the-efficacy-of-neural-planning-metrics-a#ran","syntology_url":"https://syntology.ai/paper/2010.09350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09350"}},"official":null}},{"url":"/paper/radiate-a-radar-dataset-for-automotive","slug":"radiate-a-radar-dataset-for-automotive","title":"RADIATE: A Radar Dataset for Automotive Perception in Bad Weather","date":"2020-10-18","arxiv_id":"2010.09076","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/radiate-a-radar-dataset-for-automotive#ran","syntology_url":"https://syntology.ai/paper/2010.09076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09076"}},"official":{"repos":["marcelsheeny/radiate_sdk"],"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/deformable-detr-deformable-transformers-for-1","slug":"deformable-detr-deformable-transformers-for-1","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","date":"2020-10-08","arxiv_id":"2010.04159","repositories_listed":20,"syntology":{"n":55,"n_ran":37,"n_constructed":9,"n_ran_checked":24,"n_instrument":13,"n_unverified":18,"n_honours":1,"n_violates":3,"n_no_contract":20,"n_pointer_only":21,"phrase":"37 ran (of which 9 constructed an object rather than computing a result; 24 with no instrument failure: 1 honoured, 3 violated, 20 with no contract checked; 13 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/deformable-detr-deformable-transformers-for-1#ran","syntology_url":"https://syntology.ai/paper/2010.04159","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04159"}},"official":{"repos":["fundamentalvision/Deformable-DETR"],"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/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/a-ranking-based-balanced-loss-function","slug":"a-ranking-based-balanced-loss-function","title":"A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection","date":"2020-09-28","arxiv_id":"2009.13592","repositories_listed":3,"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":1,"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/a-ranking-based-balanced-loss-function#ran","syntology_url":"https://syntology.ai/paper/2009.13592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13592"}},"official":{"repos":["kemaloksuz/aLRPLoss"],"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-images-undiscoverable-from-co-saliency","slug":"making-images-undiscoverable-from-co-saliency","title":"Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection","date":"2020-09-19","arxiv_id":"2009.09258","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":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) · 0 unverified","sample_list":"/paper/making-images-undiscoverable-from-co-saliency#ran","syntology_url":"https://syntology.ai/paper/2009.09258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09258"}},"official":{"repos":["tsingqguo/jadena"],"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/collaborative-training-between-region-1","slug":"collaborative-training-between-region-1","title":"Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection","date":"2020-09-17","arxiv_id":"2009.08119","repositories_listed":1,"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/collaborative-training-between-region-1#ran","syntology_url":"https://syntology.ai/paper/2009.08119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08119"}},"official":{"repos":["GanlongZhao/CST_DA_detection"],"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/stochastic-yolo-efficient-probabilistic","slug":"stochastic-yolo-efficient-probabilistic","title":"Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts","date":"2020-09-07","arxiv_id":"2009.02967","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"8 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/stochastic-yolo-efficient-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2009.02967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.02967"}},"official":{"repos":["tjiagom/stochastic-yolo"],"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":["official"]}}},{"url":"/paper/clocs-camera-lidar-object-candidates-fusion","slug":"clocs-camera-lidar-object-candidates-fusion","title":"CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection","date":"2020-09-02","arxiv_id":"2009.00784","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/clocs-camera-lidar-object-candidates-fusion#ran","syntology_url":"https://syntology.ai/paper/2009.00784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.00784"}},"official":{"repos":["pangsu0613/CLOCs"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rangercnn-towards-fast-and-accurate-3d-object","slug":"rangercnn-towards-fast-and-accurate-3d-object","title":"RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation","date":"2020-09-01","arxiv_id":"2009.00206","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":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/rangercnn-towards-fast-and-accurate-3d-object#ran","syntology_url":"https://syntology.ai/paper/2009.00206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.00206"}},"official":null}},{"url":"/paper/varifocalnet-an-iou-aware-dense-object","slug":"varifocalnet-an-iou-aware-dense-object","title":"VarifocalNet: An IoU-aware Dense Object Detector","date":"2020-08-31","arxiv_id":"2008.13367","repositories_listed":4,"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":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) · 0 unverified","sample_list":"/paper/varifocalnet-an-iou-aware-dense-object#ran","syntology_url":"https://syntology.ai/paper/2008.13367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.13367"}},"official":{"repos":["hyz-xmaster/VarifocalNet"],"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/regularized-densely-connected-pyramid-network","slug":"regularized-densely-connected-pyramid-network","title":"Regularized Densely-connected Pyramid Network for Salient Instance Segmentation","date":"2020-08-28","arxiv_id":"2008.12416","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/regularized-densely-connected-pyramid-network#ran","syntology_url":"https://syntology.ai/paper/2008.12416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.12416"}},"official":{"repos":["yuhuan-wu/RDPNet"],"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/deformable-pv-rcnn-improving-3d-object","slug":"deformable-pv-rcnn-improving-3d-object","title":"Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations","date":"2020-08-20","arxiv_id":"2008.08766","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/deformable-pv-rcnn-improving-3d-object#ran","syntology_url":"https://syntology.ai/paper/2008.08766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08766"}},"official":{"repos":["AutoVision-cloud/Deformable-PV-RCNN"],"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/tide-a-general-toolbox-for-identifying-object","slug":"tide-a-general-toolbox-for-identifying-object","title":"TIDE: A General Toolbox for Identifying Object Detection Errors","date":"2020-08-18","arxiv_id":"2008.08115","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":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) · 0 unverified","sample_list":"/paper/tide-a-general-toolbox-for-identifying-object#ran","syntology_url":"https://syntology.ai/paper/2008.08115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08115"}},"official":{"repos":["dbolya/tide"],"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/oriented-object-detection-in-aerial-images","slug":"oriented-object-detection-in-aerial-images","title":"Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors","date":"2020-08-17","arxiv_id":"2008.07043","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/oriented-object-detection-in-aerial-images#ran","syntology_url":"https://syntology.ai/paper/2008.07043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07043"}},"official":{"repos":["yijingru/BBAVectors-Oriented-Object-Detection"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ap-loss-for-accurate-one-stage-object","slug":"ap-loss-for-accurate-one-stage-object","title":"AP-Loss for Accurate One-Stage Object Detection","date":"2020-08-17","arxiv_id":"2008.07294","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/ap-loss-for-accurate-one-stage-object#ran","syntology_url":"https://syntology.ai/paper/2008.07294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07294"}},"official":{"repos":["cccorn/AP-loss"],"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/attack-on-multi-node-attention-for-object","slug":"attack-on-multi-node-attention-for-object","title":"Relevance Attack on Detectors","date":"2020-08-16","arxiv_id":"2008.06822","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/attack-on-multi-node-attention-for-object#ran","syntology_url":"https://syntology.ai/paper/2008.06822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06822"}},"official":{"repos":["allenchen1998/rad"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/searching-efficient-3d-architectures-with","slug":"searching-efficient-3d-architectures-with","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","date":"2020-07-31","arxiv_id":"2007.16100","repositories_listed":6,"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/searching-efficient-3d-architectures-with#ran","syntology_url":"https://syntology.ai/paper/2007.16100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.16100"}},"official":{"repos":["mit-han-lab/spvnas"],"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/weakly-supervised-3d-object-detection-from-1","slug":"weakly-supervised-3d-object-detection-from-1","title":"Weakly Supervised 3D Object Detection from Point Clouds","date":"2020-07-28","arxiv_id":"2007.13970","repositories_listed":1,"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/weakly-supervised-3d-object-detection-from-1#ran","syntology_url":"https://syntology.ai/paper/2007.13970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13970"}},"official":{"repos":["Zengyi-Qin/Weakly-Supervised-3D-Object-Detection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/accurate-low-latency-visual-perception-for","slug":"accurate-low-latency-visual-perception-for","title":"Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions","date":"2020-07-28","arxiv_id":"2007.13971","repositories_listed":1,"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/accurate-low-latency-visual-perception-for#ran","syntology_url":"https://syntology.ai/paper/2007.13971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13971"}},"official":{"repos":["cv-core/MIT-Driverless-CV-TrainingInfra"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/part-aware-data-augmentation-for-3d-object","slug":"part-aware-data-augmentation-for-3d-object","title":"Part-Aware Data Augmentation for 3D Object Detection in Point Cloud","date":"2020-07-27","arxiv_id":"2007.13373","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":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/part-aware-data-augmentation-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/2007.13373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13373"}},"official":{"repos":["sky77764/pa-aug.pytorch"],"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/accurate-rgb-d-salient-object-detection-via","slug":"accurate-rgb-d-salient-object-detection-via","title":"Accurate RGB-D Salient Object Detection via Collaborative Learning","date":"2020-07-23","arxiv_id":"2007.11782","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/accurate-rgb-d-salient-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2007.11782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11782"}},"official":{"repos":["OIPLab-DUT/CoNet"],"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/weakly-supervised-3d-object-detection-from","slug":"weakly-supervised-3d-object-detection-from","title":"Weakly Supervised 3D Object Detection from Lidar Point Cloud","date":"2020-07-23","arxiv_id":"2007.11901","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":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) · 0 unverified","sample_list":"/paper/weakly-supervised-3d-object-detection-from#ran","syntology_url":"https://syntology.ai/paper/2007.11901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11901"}},"official":{"repos":["hlesmqh/WS3D"],"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/the-devil-is-in-classification-a-simple","slug":"the-devil-is-in-classification-a-simple","title":"The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation","date":"2020-07-23","arxiv_id":"2007.11978","repositories_listed":1,"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/the-devil-is-in-classification-a-simple#ran","syntology_url":"https://syntology.ai/paper/2007.11978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11978"}},"official":{"repos":["twangnh/SimCal"],"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/few-shot-object-detection-and-viewpoint","slug":"few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","arxiv_id":"2007.12107","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-object-detection-and-viewpoint#ran","syntology_url":"https://syntology.ai/paper/2007.12107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12107"}},"official":null}},{"url":"/paper/pillar-based-object-detection-for-autonomous","slug":"pillar-based-object-detection-for-autonomous","title":"Pillar-based Object Detection for Autonomous Driving","date":"2020-07-20","arxiv_id":"2007.10323","repositories_listed":1,"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":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) · 0 unverified","sample_list":"/paper/pillar-based-object-detection-for-autonomous#ran","syntology_url":"https://syntology.ai/paper/2007.10323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10323"}},"official":{"repos":["WangYueFt/pillar-od"],"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/kinematic-3d-object-detection-in-monocular","slug":"kinematic-3d-object-detection-in-monocular","title":"Kinematic 3D Object Detection in Monocular Video","date":"2020-07-19","arxiv_id":"2007.09548","repositories_listed":2,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":16,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":2,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/kinematic-3d-object-detection-in-monocular#ran","syntology_url":"https://syntology.ai/paper/2007.09548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09548"}},"official":null}},{"url":"/paper/piou-loss-towards-accurate-oriented-object","slug":"piou-loss-towards-accurate-oriented-object","title":"PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments","date":"2020-07-19","arxiv_id":"2007.09584","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/piou-loss-towards-accurate-oriented-object#ran","syntology_url":"https://syntology.ai/paper/2007.09584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09584"}},"official":{"repos":["clobotics/piou"],"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/multi-scale-positive-sample-refinement-for","slug":"multi-scale-positive-sample-refinement-for","title":"Multi-Scale Positive Sample Refinement for Few-Shot Object Detection","date":"2020-07-18","arxiv_id":"2007.09384","repositories_listed":4,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"6 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-scale-positive-sample-refinement-for#ran","syntology_url":"https://syntology.ai/paper/2007.09384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09384"}},"official":{"repos":["jiaxi-wu/MPSR"],"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/epnet-enhancing-point-features-with-image","slug":"epnet-enhancing-point-features-with-image","title":"EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection","date":"2020-07-17","arxiv_id":"2007.08856","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/epnet-enhancing-point-features-with-image#ran","syntology_url":"https://syntology.ai/paper/2007.08856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08856"}},"official":{"repos":["happinesslz/EPNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/boosting-weakly-supervised-object-detection","slug":"boosting-weakly-supervised-object-detection","title":"Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer","date":"2020-07-15","arxiv_id":"2007.07986","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":4,"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/boosting-weakly-supervised-object-detection#ran","syntology_url":"https://syntology.ai/paper/2007.07986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07986"}},"official":{"repos":["mikuhatsune/wsod_transfer"],"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/a-single-stream-network-for-robust-and-real","slug":"a-single-stream-network-for-robust-and-real","title":"A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection","date":"2020-07-14","arxiv_id":"2007.06811","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/a-single-stream-network-for-robust-and-real#ran","syntology_url":"https://syntology.ai/paper/2007.06811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06811"}},"official":{"repos":["Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency"],"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/cross-modal-weighting-network-for-rgb-d","slug":"cross-modal-weighting-network-for-rgb-d","title":"Cross-Modal Weighting Network for RGB-D Salient Object Detection","date":"2020-07-09","arxiv_id":"2007.04901","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":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/cross-modal-weighting-network-for-rgb-d#ran","syntology_url":"https://syntology.ai/paper/2007.04901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04901"}},"official":{"repos":["MathLee/CMWNet"],"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/autoassign-differentiable-label-assignment","slug":"autoassign-differentiable-label-assignment","title":"AutoAssign: Differentiable Label Assignment for Dense Object Detection","date":"2020-07-07","arxiv_id":"2007.03496","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":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) · 1 unverified","sample_list":"/paper/autoassign-differentiable-label-assignment#ran","syntology_url":"https://syntology.ai/paper/2007.03496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03496"}},"official":{"repos":["Megvii-BaseDetection/AutoAssign"],"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/detection-as-regression-certified-object","slug":"detection-as-regression-certified-object","title":"Detection as Regression: Certified Object Detection by Median Smoothing","date":"2020-07-07","arxiv_id":"2007.03730","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/detection-as-regression-certified-object#ran","syntology_url":"https://syntology.ai/paper/2007.03730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03730"}},"official":null}},{"url":"/paper/bbs-net-rgb-d-salient-object-detection-with-a","slug":"bbs-net-rgb-d-salient-object-detection-with-a","title":"Bifurcated backbone strategy for RGB-D salient object detection","date":"2020-07-06","arxiv_id":"2007.02713","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/bbs-net-rgb-d-salient-object-detection-with-a#ran","syntology_url":"https://syntology.ai/paper/2007.02713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02713"}},"official":{"repos":["zyjwuyan/BBS-Net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/wasserstein-distances-for-stereo-disparity","slug":"wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","arxiv_id":"2007.03085","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wasserstein-distances-for-stereo-disparity#ran","syntology_url":"https://syntology.ai/paper/2007.03085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03085"}},"official":{"repos":["Div99/W-Stereo-Disp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-bottleneck-structure-for-efficient","slug":"rethinking-bottleneck-structure-for-efficient","title":"Rethinking Bottleneck Structure for Efficient Mobile Network Design","date":"2020-07-05","arxiv_id":"2007.02269","repositories_listed":4,"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":3,"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/rethinking-bottleneck-structure-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2007.02269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02269"}},"official":{"repos":["Andrew-Qibin/ssdlite-pytorch","zhoudaquan/rethinking_bottleneck_design"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-based-joint-detection-of-object-and","slug":"attention-based-joint-detection-of-object-and","title":"Attention-based Joint Detection of Object and Semantic Part","date":"2020-07-05","arxiv_id":"2007.02419","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/attention-based-joint-detection-of-object-and#ran","syntology_url":"https://syntology.ai/paper/2007.02419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02419"}},"official":{"repos":["kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-weakly-supervised-visual-grounding","slug":"improving-weakly-supervised-visual-grounding","title":"Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation","date":"2020-07-03","arxiv_id":"2007.01951","repositories_listed":1,"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/improving-weakly-supervised-visual-grounding#ran","syntology_url":"https://syntology.ai/paper/2007.01951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01951"}},"official":{"repos":["jhuang81/weak-sup-visual-grounding"],"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/rexnet-diminishing-representational","slug":"rexnet-diminishing-representational","title":"Rethinking Channel Dimensions for Efficient Model Design","date":"2020-07-02","arxiv_id":"2007.00992","repositories_listed":10,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"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, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rexnet-diminishing-representational#ran","syntology_url":"https://syntology.ai/paper/2007.00992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.00992"}},"official":{"repos":["clovaai/rexnet"],"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/afdet-anchor-free-one-stage-3d-object","slug":"afdet-anchor-free-one-stage-3d-object","title":"AFDet: Anchor Free One Stage 3D Object Detection","date":"2020-06-23","arxiv_id":"2006.12671","repositories_listed":7,"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/afdet-anchor-free-one-stage-3d-object#ran","syntology_url":"https://syntology.ai/paper/2006.12671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12671"}},"official":null}}],"record_sha256":"0e5242c99ea351294568f5b15b5dc7e014002f18bac35cc3787adecd50333d3c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}