{"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/papers/31","list_of":"/task/object","task":"Object","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":31,"pages_in_order":107,"rows_per_page":100,"rows":[3001,3100],"of":10696,"counts":{"archive_papers_tagged":10696,"with_a_code_link":3979,"where_syntology_ran_a_sample":1043,"not_listed_spam_title":0,"listed":10696,"listed_where_code_ran":1043,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":919,"every_run_a_failure_of_syntologys_instrument":124,"listed_with_a_run_with_no_instrument_failure":919,"listed_every_run_a_failure_of_syntologys_instrument":124,"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","prev":"/task/object/papers/30","next":"/task/object/papers/32","papers":[{"url":"/paper/uncertainty-for-identifying-open-set-errors","slug":"uncertainty-for-identifying-open-set-errors","title":"Uncertainty for Identifying Open-Set Errors in Visual Object Detection","date":"2021-04-03","arxiv_id":"2104.01328","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-class-suppression-loss-for-long-tail","slug":"adaptive-class-suppression-loss-for-long-tail","title":"Adaptive Class Suppression Loss for Long-Tail Object Detection","date":"2021-04-02","arxiv_id":"2104.00885","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/adaptive-class-suppression-loss-for-long-tail#ran","syntology_url":"https://syntology.ai/paper/2104.00885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00885"}},"official":{"repos":["CASIA-IVA-Lab/ACSL"],"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/decomposing-3d-scenes-into-objects-via","slug":"decomposing-3d-scenes-into-objects-via","title":"Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation","date":"2021-04-02","arxiv_id":"2104.01148","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decomposing-3d-scenes-into-objects-via#ran","syntology_url":"https://syntology.ai/paper/2104.01148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.01148"}},"official":{"repos":["stelzner/obsurf"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hvpr-hybrid-voxel-point-representation-for","slug":"hvpr-hybrid-voxel-point-representation-for","title":"HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection","date":"2021-04-02","arxiv_id":"2104.00902","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hvpr-hybrid-voxel-point-representation-for#ran","syntology_url":"https://syntology.ai/paper/2104.00902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00902"}},"official":{"repos":["cvlab-yonsei/HVPR"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/anchor-pruning-for-object-detection","slug":"anchor-pruning-for-object-detection","title":"Anchor Pruning for Object Detection","date":"2021-04-01","arxiv_id":"2104.00432","repositories_listed":1,"syntology":null},{"url":"/paper/online-multiple-object-tracking-with-cross","slug":"online-multiple-object-tracking-with-cross","title":"Online Multiple Object Tracking with Cross-Task Synergy","date":"2021-04-01","arxiv_id":"2104.00380","repositories_listed":1,"syntology":null},{"url":"/paper/repose-real-time-iterative-rendering-and","slug":"repose-real-time-iterative-rendering-and","title":"RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering","date":"2021-04-01","arxiv_id":"2104.00633","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/repose-real-time-iterative-rendering-and#ran","syntology_url":"https://syntology.ai/paper/2104.00633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00633"}},"official":{"repos":["sh8/repose"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fapis-a-few-shot-anchor-free-part-based","slug":"fapis-a-few-shot-anchor-free-part-based","title":"FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter","date":"2021-03-31","arxiv_id":"2104.00073","repositories_listed":1,"syntology":null},{"url":"/paper/groomed-nms-grouped-mathematically","slug":"groomed-nms-grouped-mathematically","title":"GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection","date":"2021-03-31","arxiv_id":"2103.17202","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/groomed-nms-grouped-mathematically#ran","syntology_url":"https://syntology.ai/paper/2103.17202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17202"}},"official":{"repos":["abhi1kumar/groomed_nms"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/scale-aware-automatic-augmentation-for-object","slug":"scale-aware-automatic-augmentation-for-object","title":"Scale-aware Automatic Augmentation for Object Detection","date":"2021-03-31","arxiv_id":"2103.17220","repositories_listed":1,"syntology":null},{"url":"/paper/soon-scenario-oriented-object-navigation-with","slug":"soon-scenario-oriented-object-navigation-with","title":"SOON: Scenario Oriented Object Navigation with Graph-based Exploration","date":"2021-03-31","arxiv_id":"2103.17138","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/soon-scenario-oriented-object-navigation-with#ran","syntology_url":"https://syntology.ai/paper/2103.17138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17138"}},"official":{"repos":["zhufengdaaa/soon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/3d-affordancenet-a-benchmark-for-visual","slug":"3d-affordancenet-a-benchmark-for-visual","title":"3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding","date":"2021-03-30","arxiv_id":"2103.16397","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/3d-affordancenet-a-benchmark-for-visual#ran","syntology_url":"https://syntology.ai/paper/2103.16397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16397"}},"official":{"repos":["Gorilla-Lab-SCUT/AffordanceNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/active-learning-for-deep-object-detection-via","slug":"active-learning-for-deep-object-detection-via","title":"Active Learning for Deep Object Detection via Probabilistic Modeling","date":"2021-03-30","arxiv_id":"2103.16130","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":0,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"phrase":"7 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; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/active-learning-for-deep-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/2103.16130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16130"}},"official":{"repos":["nvlabs/al-mdn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/delving-into-localization-errors-for","slug":"delving-into-localization-errors-for","title":"Delving into Localization Errors for Monocular 3D Object Detection","date":"2021-03-30","arxiv_id":"2103.16237","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/delving-into-localization-errors-for#ran","syntology_url":"https://syntology.ai/paper/2103.16237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16237"}},"official":{"repos":["xinzhuma/monodle"],"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/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/diagnosing-vision-and-language-navigation","slug":"diagnosing-vision-and-language-navigation","title":"Diagnosing Vision-and-Language Navigation: What Really Matters","date":"2021-03-30","arxiv_id":"2103.16561","repositories_listed":1,"syntology":null},{"url":"/paper/free-form-description-guided-3d-visual-graph","slug":"free-form-description-guided-3d-visual-graph","title":"Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud","date":"2021-03-30","arxiv_id":"2103.16381","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":3,"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/free-form-description-guided-3d-visual-graph#ran","syntology_url":"https://syntology.ai/paper/2103.16381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16381"}},"official":{"repos":["PNXD/FFL-3DOG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhanced-boundary-learning-for-glass-like","slug":"enhanced-boundary-learning-for-glass-like","title":"Enhanced Boundary Learning for Glass-like Object Segmentation","date":"2021-03-29","arxiv_id":"2103.15734","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhanced-boundary-learning-for-glass-like#ran","syntology_url":"https://syntology.ai/paper/2103.15734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15734"}},"official":{"repos":["hehao13/EBLNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sienet-spatial-information-enhancement","slug":"sienet-spatial-information-enhancement","title":"SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud","date":"2021-03-29","arxiv_id":"2103.15396","repositories_listed":1,"syntology":null},{"url":"/paper/iou-attack-towards-temporally-coherent-black","slug":"iou-attack-towards-temporally-coherent-black","title":"IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking","date":"2021-03-27","arxiv_id":"2103.14938","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":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/iou-attack-towards-temporally-coherent-black#ran","syntology_url":"https://syntology.ai/paper/2103.14938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14938"}},"official":{"repos":["VISION-SJTU/IoUattack"],"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/looking-beyond-two-frames-end-to-end-multi","slug":"looking-beyond-two-frames-end-to-end-multi","title":"Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers","date":"2021-03-27","arxiv_id":"2103.14829","repositories_listed":1,"syntology":null},{"url":"/paper/deformable-linear-object-prediction-using","slug":"deformable-linear-object-prediction-using","title":"Deformable Linear Object Prediction Using Locally Linear Latent Dynamics","date":"2021-03-26","arxiv_id":"2103.14184","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-object-detectors-via-decoupled","slug":"distilling-object-detectors-via-decoupled","title":"Distilling Object Detectors via Decoupled Features","date":"2021-03-26","arxiv_id":"2103.14475","repositories_listed":1,"syntology":null},{"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/non-salient-region-object-mining-for-weakly","slug":"non-salient-region-object-mining-for-weakly","title":"Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation","date":"2021-03-26","arxiv_id":"2103.14581","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/non-salient-region-object-mining-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2103.14581","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14581"}},"official":{"repos":["NUST-Machine-Intelligence-Laboratory/nsrom"],"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/omnihang-learning-to-hang-arbitrary-objects","slug":"omnihang-learning-to-hang-arbitrary-objects","title":"OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation","date":"2021-03-26","arxiv_id":"2103.14283","repositories_listed":1,"syntology":null},{"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/real-time-and-accurate-object-detection-in","slug":"real-time-and-accurate-object-detection-in","title":"Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation","date":"2021-03-25","arxiv_id":"2103.14529","repositories_listed":1,"syntology":null},{"url":"/paper/usb-universal-scale-object-detection","slug":"usb-universal-scale-object-detection","title":"USB: Universal-Scale Object Detection Benchmark","date":"2021-03-25","arxiv_id":"2103.14027","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-regional-memory-network-for-video","slug":"efficient-regional-memory-network-for-video","title":"Efficient Regional Memory Network for Video Object Segmentation","date":"2021-03-24","arxiv_id":"2103.12934","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/efficient-regional-memory-network-for-video#ran","syntology_url":"https://syntology.ai/paper/2103.12934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12934"}},"official":{"repos":["hzxie/RMNet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/fakemix-augmentation-improves-transparent","slug":"fakemix-augmentation-improves-transparent","title":"FakeMix Augmentation Improves Transparent Object Detection","date":"2021-03-24","arxiv_id":"2103.13279","repositories_listed":1,"syntology":null},{"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/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/relation-aware-instance-refinement-for-weakly","slug":"relation-aware-instance-refinement-for-weakly","title":"Relation-aware Instance Refinement for Weakly Supervised Visual Grounding","date":"2021-03-24","arxiv_id":"2103.12989","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/relation-aware-instance-refinement-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2103.12989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12989"}},"official":{"repos":["youngfly11/ReIR-WeaklyGrounding.pytorch"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"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/rpattack-refined-patch-attack-on-general","slug":"rpattack-refined-patch-attack-on-general","title":"RPATTACK: Refined Patch Attack on General Object Detectors","date":"2021-03-23","arxiv_id":"2103.12469","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-layout-to-image-generation-with","slug":"context-aware-layout-to-image-generation-with","title":"Context-Aware Layout to Image Generation with Enhanced Object Appearance","date":"2021-03-22","arxiv_id":"2103.11897","repositories_listed":1,"syntology":null},{"url":"/paper/optimization-for-oriented-object-detection","slug":"optimization-for-oriented-object-detection","title":"Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss","date":"2021-03-22","arxiv_id":"2103.11636","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-meets-tracker-exploiting-temporal","slug":"transformer-meets-tracker-exploiting-temporal","title":"Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking","date":"2021-03-22","arxiv_id":"2103.11681","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/transformer-meets-tracker-exploiting-temporal#ran","syntology_url":"https://syntology.ai/paper/2103.11681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11681"}},"official":{"repos":["594422814/TransformerTrack"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/instant-teaching-an-end-to-end-semi","slug":"instant-teaching-an-end-to-end-semi","title":"Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework","date":"2021-03-21","arxiv_id":"2103.11402","repositories_listed":1,"syntology":null},{"url":"/paper/learning-calibrated-guidance-for-object","slug":"learning-calibrated-guidance-for-object","title":"Learning Calibrated-Guidance for Object Detection in Aerial Images","date":"2021-03-21","arxiv_id":"2103.11399","repositories_listed":1,"syntology":null},{"url":"/paper/uav-images-dataset-for-moving-object","slug":"uav-images-dataset-for-moving-object","title":"UAV Images Dataset for Moving Object Detection from Moving Cameras","date":"2021-03-21","arxiv_id":"2103.11460","repositories_listed":1,"syntology":null},{"url":"/paper/ce-fpn-enhancing-channel-information-for","slug":"ce-fpn-enhancing-channel-information-for","title":"CE-FPN: Enhancing Channel Information for Object Detection","date":"2021-03-19","arxiv_id":"2103.10643","repositories_listed":1,"syntology":null},{"url":"/paper/hopper-multi-hop-transformer-for-1","slug":"hopper-multi-hop-transformer-for-1","title":"Hopper: Multi-hop Transformer for Spatiotemporal Reasoning","date":"2021-03-19","arxiv_id":"2103.10574","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-guided-object-discovery-with","slug":"knowledge-guided-object-discovery-with","title":"Knowledge-Guided Object Discovery with Acquired Deep Impressions","date":"2021-03-19","arxiv_id":"2103.10611","repositories_listed":1,"syntology":null},{"url":"/paper/tdiot-target-driven-inference-for-deep-video","slug":"tdiot-target-driven-inference-for-deep-video","title":"TDIOT: Target-driven Inference for Deep Video Object Tracking","date":"2021-03-19","arxiv_id":"2103.11017","repositories_listed":1,"syntology":null},{"url":"/paper/articulated-object-interaction-in-unknown","slug":"articulated-object-interaction-in-unknown","title":"Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation","date":"2021-03-18","arxiv_id":"2103.10534","repositories_listed":1,"syntology":null},{"url":"/paper/consistency-based-active-learning-for-object","slug":"consistency-based-active-learning-for-object","title":"Consistency-based Active Learning for Object Detection","date":"2021-03-18","arxiv_id":"2103.10374","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-loss-weight-adjustment-in","slug":"revisiting-the-loss-weight-adjustment-in","title":"Revisiting the Loss Weight Adjustment in Object Detection","date":"2021-03-17","arxiv_id":"2103.09488","repositories_listed":1,"syntology":null},{"url":"/paper/anti-adversarially-manipulated-attributions","slug":"anti-adversarially-manipulated-attributions","title":"Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation","date":"2021-03-16","arxiv_id":"2103.08896","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/anti-adversarially-manipulated-attributions#ran","syntology_url":"https://syntology.ai/paper/2103.08896","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08896"}},"official":{"repos":["jbeomlee93/AdvCAM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bbam-bounding-box-attribution-map-for-weakly","slug":"bbam-bounding-box-attribution-map-for-weakly","title":"BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation","date":"2021-03-16","arxiv_id":"2103.08907","repositories_listed":1,"syntology":null},{"url":"/paper/simultaneous-multi-view-camera-pose","slug":"simultaneous-multi-view-camera-pose","title":"Simultaneous Multi-View Camera Pose Estimation and Object Tracking with Square Planar Markers","date":"2021-03-16","arxiv_id":"2103.09141","repositories_listed":1,"syntology":null},{"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/detecting-human-object-interaction-via","slug":"detecting-human-object-interaction-via","title":"Detecting Human-Object Interaction via Fabricated Compositional Learning","date":"2021-03-15","arxiv_id":"2103.08214","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/detecting-human-object-interaction-via#ran","syntology_url":"https://syntology.ai/paper/2103.08214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08214"}},"official":{"repos":["zhihou7/HOI-CL"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/group-collaborative-learning-for-co-salient","slug":"group-collaborative-learning-for-co-salient","title":"Group Collaborative Learning for Co-Salient Object Detection","date":"2021-03-15","arxiv_id":"2104.01108","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-proposal-classifier-for-multiple","slug":"learning-a-proposal-classifier-for-multiple","title":"Learning a Proposal Classifier for Multiple Object Tracking","date":"2021-03-14","arxiv_id":"2103.07889","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"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 4 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; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-a-proposal-classifier-for-multiple#ran","syntology_url":"https://syntology.ai/paper/2103.07889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.07889"}},"official":{"repos":["daip13/LPC_MOT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monocular-quasi-dense-3d-object-tracking","slug":"monocular-quasi-dense-3d-object-tracking","title":"Monocular Quasi-Dense 3D Object Tracking","date":"2021-03-12","arxiv_id":"2103.07351","repositories_listed":1,"syntology":null},{"url":"/paper/dualposenet-category-level-6d-object-pose-and","slug":"dualposenet-category-level-6d-object-pose-and","title":"DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency","date":"2021-03-11","arxiv_id":"2103.06526","repositories_listed":1,"syntology":null},{"url":"/paper/holistic-3d-scene-understanding-from-a-single-1","slug":"holistic-3d-scene-understanding-from-a-single-1","title":"Holistic 3D Scene Understanding from a Single Image with Implicit Representation","date":"2021-03-11","arxiv_id":"2103.06422","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-of-face-obfuscation-in-imagenet","slug":"a-study-of-face-obfuscation-in-imagenet","title":"A Study of Face Obfuscation in ImageNet","date":"2021-03-10","arxiv_id":"2103.06191","repositories_listed":1,"syntology":null},{"url":"/paper/patchnet-short-range-template-matching-for","slug":"patchnet-short-range-template-matching-for","title":"PatchNet -- Short-range Template Matching for Efficient Video Processing","date":"2021-03-10","arxiv_id":"2103.07371","repositories_listed":1,"syntology":null},{"url":"/paper/st3d-self-training-for-unsupervised-domain","slug":"st3d-self-training-for-unsupervised-domain","title":"ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection","date":"2021-03-09","arxiv_id":"2103.05346","repositories_listed":1,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 5 unverified","sample_list":"/paper/st3d-self-training-for-unsupervised-domain#ran","syntology_url":"https://syntology.ai/paper/2103.05346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05346"}},"official":{"repos":["CVMI-Lab/ST3D"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/contemplating-real-world-object","slug":"contemplating-real-world-object","title":"Contemplating real-world object classification","date":"2021-03-08","arxiv_id":"2103.05137","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/contemplating-real-world-object#ran","syntology_url":"https://syntology.ai/paper/2103.05137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05137"}},"official":{"repos":["aliborji/ObjectNetReanalysis"],"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/unveiling-the-potential-of-structure","slug":"unveiling-the-potential-of-structure","title":"Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization","date":"2021-03-08","arxiv_id":"2103.04523","repositories_listed":1,"syntology":null},{"url":"/paper/simple-online-and-real-time-tracking-with","slug":"simple-online-and-real-time-tracking-with","title":"Simple online and real-time tracking with occlusion handling","date":"2021-03-06","arxiv_id":"2103.04147","repositories_listed":1,"syntology":null},{"url":"/paper/fast-interactive-video-object-segmentation","slug":"fast-interactive-video-object-segmentation","title":"Fast Interactive Video Object Segmentation with Graph Neural Networks","date":"2021-03-05","arxiv_id":"2103.03821","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-object-detection-via","slug":"data-augmentation-for-object-detection-via","title":"Data Augmentation for Object Detection via Differentiable Neural Rendering","date":"2021-03-04","arxiv_id":"2103.02852","repositories_listed":1,"syntology":null},{"url":"/paper/general-instance-distillation-for-object","slug":"general-instance-distillation-for-object","title":"General Instance Distillation for Object Detection","date":"2021-03-03","arxiv_id":"2103.02340","repositories_listed":1,"syntology":null},{"url":"/paper/cloudaae-learning-6d-object-pose-regression","slug":"cloudaae-learning-6d-object-pose-regression","title":"CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds","date":"2021-03-02","arxiv_id":"2103.01977","repositories_listed":1,"syntology":null},{"url":"/paper/context-decoupling-augmentation-for-weakly","slug":"context-decoupling-augmentation-for-weakly","title":"Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation","date":"2021-03-02","arxiv_id":"2103.01795","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/context-decoupling-augmentation-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2103.01795","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01795"}},"official":{"repos":["suyukun666/CDA"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/multiple-convolutional-features-in-siamese","slug":"multiple-convolutional-features-in-siamese","title":"Multiple Convolutional Features in Siamese Networks for Object Tracking","date":"2021-03-01","arxiv_id":"2103.01222","repositories_listed":1,"syntology":null},{"url":"/paper/universal-prototype-augmentation-for-few-shot","slug":"universal-prototype-augmentation-for-few-shot","title":"Universal-Prototype Enhancing for Few-Shot Object Detection","date":"2021-03-01","arxiv_id":"2103.01077","repositories_listed":1,"syntology":null},{"url":"/paper/object-affordance-as-a-guide-for-grasp-type","slug":"object-affordance-as-a-guide-for-grasp-type","title":"Text-driven object affordance for guiding grasp-type recognition in multimodal robot teaching","date":"2021-02-27","arxiv_id":"2103.00268","repositories_listed":1,"syntology":null},{"url":"/paper/prior-image-constrained-reconstruction-using","slug":"prior-image-constrained-reconstruction-using","title":"Prior Image-Constrained Reconstruction using Style-Based Generative Models","date":"2021-02-24","arxiv_id":"2102.12525","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/prior-image-constrained-reconstruction-using#ran","syntology_url":"https://syntology.ai/paper/2102.12525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12525"}},"official":{"repos":["comp-imaging-sci/pic-recon"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/should-i-look-at-the-head-or-the-tail-dual","slug":"should-i-look-at-the-head-or-the-tail-dual","title":"Dual-Awareness Attention for Few-Shot Object Detection","date":"2021-02-24","arxiv_id":"2102.12152","repositories_listed":1,"syntology":null},{"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/densely-nested-top-down-flows-for-salient","slug":"densely-nested-top-down-flows-for-salient","title":"Densely Nested Top-Down Flows for Salient Object Detection","date":"2021-02-18","arxiv_id":"2102.09133","repositories_listed":1,"syntology":null},{"url":"/paper/separable-structure-modeling-for-semi","slug":"separable-structure-modeling-for-semi","title":"Separable Structure Modeling for Semi-supervised Video Object Segmentation","date":"2021-02-18","arxiv_id":null,"repositories_listed":1,"syntology":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/instance-localization-for-self-supervised","slug":"instance-localization-for-self-supervised","title":"Instance Localization for Self-supervised Detection Pretraining","date":"2021-02-16","arxiv_id":"2102.08318","repositories_listed":1,"syntology":null},{"url":"/paper/densely-deformable-efficient-salient-object","slug":"densely-deformable-efficient-salient-object","title":"Densely Deformable Efficient Salient Object Detection Network","date":"2021-02-12","arxiv_id":"2102.06407","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-3d-surface-manifolds-with-a-locally","slug":"modeling-3d-surface-manifolds-with-a-locally","title":"Modeling 3D Surface Manifolds with a Locally Conditioned Atlas","date":"2021-02-11","arxiv_id":"2102.05984","repositories_listed":1,"syntology":null},{"url":"/paper/scale-normalized-image-pyramids-with","slug":"scale-normalized-image-pyramids-with","title":"Scale Normalized Image Pyramids with AutoFocus for Object Detection","date":"2021-02-10","arxiv_id":"2102.05646","repositories_listed":1,"syntology":null},{"url":"/paper/rodnet-a-real-time-radar-object-detection","slug":"rodnet-a-real-time-radar-object-detection","title":"RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization","date":"2021-02-09","arxiv_id":"2102.05150","repositories_listed":1,"syntology":null},{"url":"/paper/swiftnet-real-time-video-object-segmentation","slug":"swiftnet-real-time-video-object-segmentation","title":"SwiftNet: Real-time Video Object Segmentation","date":"2021-02-09","arxiv_id":"2102.04604","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/swiftnet-real-time-video-object-segmentation#ran","syntology_url":"https://syntology.ai/paper/2102.04604","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04604"}},"official":{"repos":["haochenheheda/SwiftNet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sill-net-feature-augmentation-with-separated","slug":"sill-net-feature-augmentation-with-separated","title":"Sill-Net: Feature Augmentation with Separated Illumination Representation","date":"2021-02-06","arxiv_id":"2102.03539","repositories_listed":1,"syntology":null},{"url":"/paper/detectorguard-provably-securing-object","slug":"detectorguard-provably-securing-object","title":"DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks","date":"2021-02-05","arxiv_id":"2102.02956","repositories_listed":1,"syntology":null},{"url":"/paper/compressed-object-detection","slug":"compressed-object-detection","title":"Compressed Object Detection","date":"2021-02-04","arxiv_id":"2102.02896","repositories_listed":1,"syntology":null},{"url":"/paper/deft-detection-embeddings-for-tracking","slug":"deft-detection-embeddings-for-tracking","title":"DEFT: Detection Embeddings for Tracking","date":"2021-02-03","arxiv_id":"2102.02267","repositories_listed":1,"syntology":null},{"url":"/paper/ground-aware-monocular-3d-object-detection","slug":"ground-aware-monocular-3d-object-detection","title":"Ground-aware Monocular 3D Object Detection for Autonomous Driving","date":"2021-02-01","arxiv_id":"2102.00690","repositories_listed":1,"syntology":null},{"url":"/paper/inferring-spatial-relations-from-textual","slug":"inferring-spatial-relations-from-textual","title":"Inferring spatial relations from textual descriptions of images","date":"2021-02-01","arxiv_id":"2102.00997","repositories_listed":1,"syntology":null},{"url":"/paper/pv-rcnn-point-voxel-feature-set-abstraction-1","slug":"pv-rcnn-point-voxel-feature-set-abstraction-1","title":"PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection","date":"2021-01-31","arxiv_id":"2102.00463","repositories_listed":1,"syntology":null},{"url":"/paper/re-on-end-to-end-6-dof-object-pose-estimation","slug":"re-on-end-to-end-6-dof-object-pose-estimation","title":"[Re] On end-to-end 6{DoF} object pose estimation and robustness to object scale","date":"2021-01-31","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-representation-learning-for-5","slug":"self-supervised-representation-learning-for-5","title":"Self-Supervised Pretraining for RGB-D Salient Object Detection","date":"2021-01-29","arxiv_id":"2101.12482","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-appearance-modeling-with-multi","slug":"discriminative-appearance-modeling-with-multi","title":"Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking","date":"2021-01-28","arxiv_id":"2101.12159","repositories_listed":1,"syntology":null},{"url":"/paper/transferable-interactiveness-knowledge-for","slug":"transferable-interactiveness-knowledge-for","title":"Transferable Interactiveness Knowledge for Human-Object Interaction Detection","date":"2021-01-25","arxiv_id":"2101.10292","repositories_listed":1,"syntology":null},{"url":"/paper/personal-fixations-based-object-segmentation","slug":"personal-fixations-based-object-segmentation","title":"Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation","date":"2021-01-22","arxiv_id":"2101.09014","repositories_listed":1,"syntology":null},{"url":"/paper/all-day-object-tracking-for-unmanned-aerial","slug":"all-day-object-tracking-for-unmanned-aerial","title":"All-Day Object Tracking for Unmanned Aerial Vehicle","date":"2021-01-21","arxiv_id":"2101.08446","repositories_listed":1,"syntology":null},{"url":"/paper/sstvos-sparse-spatiotemporal-transformers-for","slug":"sstvos-sparse-spatiotemporal-transformers-for","title":"SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation","date":"2021-01-21","arxiv_id":"2101.08833","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 5 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) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/sstvos-sparse-spatiotemporal-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2101.08833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.08833"}},"official":{"repos":["dukebw/SSTVOS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/1st-place-solution-to-eccv-tao-2020-detect","slug":"1st-place-solution-to-eccv-tao-2020-detect","title":"1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking","date":"2021-01-20","arxiv_id":"2101.08040","repositories_listed":1,"syntology":null}],"record_sha256":"fc1f697010ada50645f0b8efd418229844bbb557b90b0e0d03a34687fad0db9e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}