{"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/7","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":7,"pages_in_order":107,"rows_per_page":100,"rows":[601,700],"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/6","next":"/task/object/papers/8","papers":[{"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/shape-prior-deformation-for-categorical-6d","slug":"shape-prior-deformation-for-categorical-6d","title":"Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation","date":"2020-07-16","arxiv_id":"2007.08454","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/shape-prior-deformation-for-categorical-6d#ran","syntology_url":"https://syntology.ai/paper/2007.08454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08454"}},"official":{"repos":["mentian/object-deformnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/centernet3d-an-anchor-free-object-detector","slug":"centernet3d-an-anchor-free-object-detector","title":"CenterNet3D: An Anchor Free Object Detector for Point Cloud","date":"2020-07-13","arxiv_id":"2007.07214","repositories_listed":2,"syntology":null},{"url":"/paper/part-aware-prototype-network-for-few-shot","slug":"part-aware-prototype-network-for-few-shot","title":"Part-aware Prototype Network for Few-shot Semantic Segmentation","date":"2020-07-13","arxiv_id":"2007.06309","repositories_listed":2,"syntology":null},{"url":"/paper/learning-object-depth-from-camera-motion-and","slug":"learning-object-depth-from-camera-motion-and","title":"Learning Object Depth from Camera Motion and Video Object Segmentation","date":"2020-07-11","arxiv_id":"2007.05676","repositories_listed":2,"syntology":null},{"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/re-thinking-co-salient-object-detection","slug":"re-thinking-co-salient-object-detection","title":"Re-thinking Co-Salient Object Detection","date":"2020-07-07","arxiv_id":"2007.03380","repositories_listed":2,"syntology":null},{"url":"/paper/rgbt-salient-object-detection-a-large-scale","slug":"rgbt-salient-object-detection-a-large-scale","title":"RGBT Salient Object Detection: A Large-scale Dataset and Benchmark","date":"2020-07-07","arxiv_id":"2007.03262","repositories_listed":2,"syntology":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/houghnet-integrating-near-and-long-range","slug":"houghnet-integrating-near-and-long-range","title":"HoughNet: Integrating near and long-range evidence for bottom-up object detection","date":"2020-07-05","arxiv_id":"2007.02355","repositories_listed":2,"syntology":null},{"url":"/paper/mining-cross-image-semantics-for-weakly","slug":"mining-cross-image-semantics-for-weakly","title":"Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation","date":"2020-07-03","arxiv_id":"2007.01947","repositories_listed":2,"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/mining-cross-image-semantics-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2007.01947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01947"}},"official":{"repos":["GuoleiSun/MCIS_wsss"],"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/object-goal-navigation-using-goal-oriented","slug":"object-goal-navigation-using-goal-oriented","title":"Object Goal Navigation using Goal-Oriented Semantic Exploration","date":"2020-07-01","arxiv_id":"2007.00643","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/object-goal-navigation-using-goal-oriented#ran","syntology_url":"https://syntology.ai/paper/2007.00643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.00643"}},"official":{"repos":["devendrachaplot/Object-Goal-Navigation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/overcoming-classifier-imbalance-for-long-tail-1","slug":"overcoming-classifier-imbalance-for-long-tail-1","title":"Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax","date":"2020-06-18","arxiv_id":"2006.10408","repositories_listed":2,"syntology":null},{"url":"/paper/3d-reconstruction-of-novel-object-shapes-from","slug":"3d-reconstruction-of-novel-object-shapes-from","title":"3D Reconstruction of Novel Object Shapes from Single Images","date":"2020-06-14","arxiv_id":"2006.07752","repositories_listed":2,"syntology":null},{"url":"/paper/codenet-algorithm-hardware-co-design-for","slug":"codenet-algorithm-hardware-co-design-for","title":"CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs","date":"2020-06-12","arxiv_id":"2006.08357","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-pre-training-and-self-training","slug":"rethinking-pre-training-and-self-training","title":"Rethinking Pre-training and Self-training","date":"2020-06-11","arxiv_id":"2006.06882","repositories_listed":2,"syntology":null},{"url":"/paper/h3dnet-3d-object-detection-using-hybrid","slug":"h3dnet-3d-object-detection-using-hybrid","title":"H3DNet: 3D Object Detection Using Hybrid Geometric Primitives","date":"2020-06-10","arxiv_id":"2006.05682","repositories_listed":2,"syntology":null},{"url":"/paper/black-box-explanation-of-object-detectors-via","slug":"black-box-explanation-of-object-detectors-via","title":"Black-box Explanation of Object Detectors via Saliency Maps","date":"2020-06-05","arxiv_id":"2006.03204","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/black-box-explanation-of-object-detectors-via#ran","syntology_url":"https://syntology.ai/paper/2006.03204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03204"}},"official":null}},{"url":"/paper/arshadowgan-shadow-generative-adversarial","slug":"arshadowgan-shadow-generative-adversarial","title":"ARShadowGAN: Shadow Generative Adversarial Network for Augmented Reality in Single Light Scenes","date":"2020-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/camouflaged-object-detection","slug":"camouflaged-object-detection","title":"Camouflaged Object Detection","date":"2020-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/interactive-object-segmentation-with-inside","slug":"interactive-object-segmentation-with-inside","title":"Interactive Object Segmentation With Inside-Outside Guidance","date":"2020-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/predicting-goal-directed-human-attention","slug":"predicting-goal-directed-human-attention","title":"Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning","date":"2020-05-28","arxiv_id":"2005.14310","repositories_listed":2,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/predicting-goal-directed-human-attention#ran","syntology_url":"https://syntology.ai/paper/2005.14310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.14310"}},"official":{"repos":["cvlab-stonybrook/Scanpath_Prediction"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-guided-context-feature-pyramid","slug":"attention-guided-context-feature-pyramid","title":"Attention-guided Context Feature Pyramid Network for Object Detection","date":"2020-05-23","arxiv_id":"2005.11475","repositories_listed":2,"syntology":null},{"url":"/paper/instance-aware-image-colorization","slug":"instance-aware-image-colorization","title":"Instance-aware Image Colorization","date":"2020-05-21","arxiv_id":"2005.10825","repositories_listed":2,"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":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/instance-aware-image-colorization#ran","syntology_url":"https://syntology.ai/paper/2005.10825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10825"}},"official":{"repos":["ericsujw/InstColorization"],"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/scale-equalizing-pyramid-convolution-for","slug":"scale-equalizing-pyramid-convolution-for","title":"Scale-Equalizing Pyramid Convolution for Object Detection","date":"2020-05-06","arxiv_id":"2005.03101","repositories_listed":2,"syntology":null},{"url":"/paper/the-epic-kitchens-dataset-collection","slug":"the-epic-kitchens-dataset-collection","title":"The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines","date":"2020-04-29","arxiv_id":"2005.00343","repositories_listed":2,"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/the-epic-kitchens-dataset-collection#ran","syntology_url":"https://syntology.ai/paper/2005.00343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00343"}},"official":{"repos":["epic-kitchens/action-models"],"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/corenet-coherent-3d-scene-reconstruction-from","slug":"corenet-coherent-3d-scene-reconstruction-from","title":"CoReNet: Coherent 3D scene reconstruction from a single RGB image","date":"2020-04-27","arxiv_id":"2004.12989","repositories_listed":2,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/corenet-coherent-3d-scene-reconstruction-from#ran","syntology_url":"https://syntology.ai/paper/2004.12989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12989"}},"official":{"repos":["google-research/corenet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/eao-slam-monocular-semi-dense-object-slam","slug":"eao-slam-monocular-semi-dense-object-slam","title":"EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association","date":"2020-04-27","arxiv_id":"2004.12730","repositories_listed":2,"syntology":null},{"url":"/paper/instance-segmentation-of-biomedical-images","slug":"instance-segmentation-of-biomedical-images","title":"Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints","date":"2020-04-21","arxiv_id":"2004.09821","repositories_listed":2,"syntology":null},{"url":"/paper/instance-aware-context-focused-and-memory","slug":"instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","date":"2020-04-09","arxiv_id":"2004.04725","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/instance-aware-context-focused-and-memory#ran","syntology_url":"https://syntology.ai/paper/2004.04725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04725"}},"official":{"repos":["NVlabs/wetectron"],"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/tog-targeted-adversarial-objectness-gradient","slug":"tog-targeted-adversarial-objectness-gradient","title":"TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems","date":"2020-04-09","arxiv_id":"2004.04320","repositories_listed":2,"syntology":null},{"url":"/paper/self-supervised-viewpoint-learning-from-image","slug":"self-supervised-viewpoint-learning-from-image","title":"Self-Supervised Viewpoint Learning From Image Collections","date":"2020-04-03","arxiv_id":"2004.01793","repositories_listed":2,"syntology":null},{"url":"/paper/look-into-object-self-supervised-structure","slug":"look-into-object-self-supervised-structure","title":"Look-into-Object: Self-supervised Structure Modeling for Object Recognition","date":"2020-03-31","arxiv_id":"2003.14142","repositories_listed":2,"syntology":null},{"url":"/paper/squeezed-deep-6dof-object-detection-using","slug":"squeezed-deep-6dof-object-detection-using","title":"Squeezed Deep 6DoF Object Detection Using Knowledge Distillation","date":"2020-03-30","arxiv_id":"2003.13586","repositories_listed":2,"syntology":null},{"url":"/paper/memory-enhanced-global-local-aggregation-for","slug":"memory-enhanced-global-local-aggregation-for","title":"Memory Enhanced Global-Local Aggregation for Video Object Detection","date":"2020-03-26","arxiv_id":"2003.12063","repositories_listed":2,"syntology":null},{"url":"/paper/learning-what-to-learn-for-video-object","slug":"learning-what-to-learn-for-video-object","title":"Learning What to Learn for Video Object Segmentation","date":"2020-03-25","arxiv_id":"2003.11540","repositories_listed":2,"syntology":null},{"url":"/paper/collaborative-video-object-segmentation-by","slug":"collaborative-video-object-segmentation-by","title":"Collaborative Video Object Segmentation by Foreground-Background Integration","date":"2020-03-18","arxiv_id":"2003.08333","repositories_listed":2,"syntology":null},{"url":"/paper/1st-place-solutions-for-openimage2019-object","slug":"1st-place-solutions-for-openimage2019-object","title":"1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation","date":"2020-03-17","arxiv_id":"2003.07557","repositories_listed":2,"syntology":null},{"url":"/paper/incremental-object-detection-via-meta","slug":"incremental-object-detection-via-meta","title":"Incremental Object Detection via Meta-Learning","date":"2020-03-17","arxiv_id":"2003.08798","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"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) · 2 unverified","sample_list":"/paper/incremental-object-detection-via-meta#ran","syntology_url":"https://syntology.ai/paper/2003.08798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.08798"}},"official":{"repos":["JosephKJ/iOD"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/revisiting-the-sibling-head-in-object","slug":"revisiting-the-sibling-head-in-object","title":"Revisiting the Sibling Head in Object Detector","date":"2020-03-17","arxiv_id":"2003.07540","repositories_listed":2,"syntology":null},{"url":"/paper/vsgnet-spatial-attention-network-for","slug":"vsgnet-spatial-attention-network-for","title":"VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions","date":"2020-03-11","arxiv_id":"2003.05541","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/vsgnet-spatial-attention-network-for#ran","syntology_url":"https://syntology.ai/paper/2003.05541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05541"}},"official":{"repos":["ASMIftekhar/VSGNet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bidet-an-efficient-binarized-object-detector","slug":"bidet-an-efficient-binarized-object-detector","title":"BiDet: An Efficient Binarized Object Detector","date":"2020-03-09","arxiv_id":"2003.03961","repositories_listed":2,"syntology":null},{"url":"/paper/birdnet-end-to-end-3d-object-detection-in","slug":"birdnet-end-to-end-3d-object-detection-in","title":"BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View","date":"2020-03-09","arxiv_id":"2003.04188","repositories_listed":2,"syntology":null},{"url":"/paper/image-generation-from-freehand-scene-sketches","slug":"image-generation-from-freehand-scene-sketches","title":"SketchyCOCO: Image Generation from Freehand Scene Sketches","date":"2020-03-05","arxiv_id":"2003.02683","repositories_listed":2,"syntology":null},{"url":"/paper/plug-play-convolutional-regression-tracker","slug":"plug-play-convolutional-regression-tracker","title":"Plug & Play Convolutional Regression Tracker for Video Object Detection","date":"2020-03-02","arxiv_id":"2003.00981","repositories_listed":2,"syntology":null},{"url":"/paper/3dssd-point-based-3d-single-stage-object","slug":"3dssd-point-based-3d-single-stage-object","title":"3DSSD: Point-based 3D Single Stage Object Detector","date":"2020-02-24","arxiv_id":"2002.10187","repositories_listed":2,"syntology":null},{"url":"/paper/solving-missing-annotation-object-detection","slug":"solving-missing-annotation-object-detection","title":"Solving Missing-Annotation Object Detection with Background Recalibration Loss","date":"2020-02-12","arxiv_id":"2002.05274","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/solving-missing-annotation-object-detection#ran","syntology_url":"https://syntology.ai/paper/2002.05274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05274"}},"official":{"repos":["Dwrety/mmdetection-selective-iou"],"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/evaluating-weakly-supervised-object","slug":"evaluating-weakly-supervised-object","title":"Evaluating Weakly Supervised Object Localization Methods Right","date":"2020-01-21","arxiv_id":"2001.07437","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluating-weakly-supervised-object#ran","syntology_url":"https://syntology.ai/paper/2001.07437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.07437"}},"official":{"repos":["clovaai/wsolevaluation"],"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/vision-meets-drones-past-present-and-future","slug":"vision-meets-drones-past-present-and-future","title":"Detection and Tracking Meet Drones Challenge","date":"2020-01-16","arxiv_id":"2001.06303","repositories_listed":2,"syntology":null},{"url":"/paper/combining-deep-learning-and-verification-for","slug":"combining-deep-learning-and-verification-for","title":"Combining Deep Learning and Verification for Precise Object Instance Detection","date":"2019-12-27","arxiv_id":"1912.12270","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":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/combining-deep-learning-and-verification-for#ran","syntology_url":"https://syntology.ai/paper/1912.12270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.12270"}},"official":{"repos":["siddancha/FlowVerify"],"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/category-level-articulated-object-pose","slug":"category-level-articulated-object-pose","title":"Category-Level Articulated Object Pose Estimation","date":"2019-12-26","arxiv_id":"1912.11913","repositories_listed":2,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/category-level-articulated-object-pose#ran","syntology_url":"https://syntology.ai/paper/1912.11913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11913"}},"official":{"repos":["dragonlong/articulated-pose"],"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/dense-reppoints-representing-visual-objects","slug":"dense-reppoints-representing-visual-objects","title":"Dense RepPoints: Representing Visual Objects with Dense Point Sets","date":"2019-12-24","arxiv_id":"1912.11473","repositories_listed":2,"syntology":null},{"url":"/paper/scale-match-for-tiny-person-detection","slug":"scale-match-for-tiny-person-detection","title":"Scale Match for Tiny Person Detection","date":"2019-12-23","arxiv_id":"1912.10664","repositories_listed":2,"syntology":null},{"url":"/paper/learning-a-neural-solver-for-multiple-object","slug":"learning-a-neural-solver-for-multiple-object","title":"Learning a Neural Solver for Multiple Object Tracking","date":"2019-12-16","arxiv_id":"1912.07515","repositories_listed":2,"syntology":{"n":20,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"16 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-a-neural-solver-for-multiple-object#ran","syntology_url":"https://syntology.ai/paper/1912.07515","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.07515"}},"official":null}},{"url":"/paper/solving-visual-object-ambiguities-when","slug":"solving-visual-object-ambiguities-when","title":"Solving Visual Object Ambiguities when Pointing: An Unsupervised Learning Approach","date":"2019-12-13","arxiv_id":"1912.06449","repositories_listed":2,"syntology":null},{"url":"/paper/iou-aware-single-stage-object-detector-for","slug":"iou-aware-single-stage-object-detector-for","title":"IoU-aware Single-stage Object Detector for Accurate Localization","date":"2019-12-12","arxiv_id":"1912.05992","repositories_listed":2,"syntology":null},{"url":"/paper/augfpn-improving-multi-scale-feature-learning","slug":"augfpn-improving-multi-scale-feature-learning","title":"AugFPN: Improving Multi-scale Feature Learning for Object Detection","date":"2019-12-11","arxiv_id":"1912.05384","repositories_listed":2,"syntology":null},{"url":"/paper/tanet-robust-3d-object-detection-from-point","slug":"tanet-robust-3d-object-detection-from-point","title":"TANet: Robust 3D Object Detection from Point Clouds with Triple Attention","date":"2019-12-11","arxiv_id":"1912.05163","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/tanet-robust-3d-object-detection-from-point#ran","syntology_url":"https://syntology.ai/paper/1912.05163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05163"}},"official":null}},{"url":"/paper/learning-depth-guided-convolutions-for","slug":"learning-depth-guided-convolutions-for","title":"Learning Depth-Guided Convolutions for Monocular 3D Object Detection","date":"2019-12-10","arxiv_id":"1912.04799","repositories_listed":2,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 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; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/learning-depth-guided-convolutions-for#ran","syntology_url":"https://syntology.ai/paper/1912.04799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.04799"}},"official":{"repos":["dingmyu/D4LCN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/consistency-based-semi-supervised-learning","slug":"consistency-based-semi-supervised-learning","title":"Consistency-based Semi-supervised Learning for Object detection","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/soft-anchor-point-object-detection","slug":"soft-anchor-point-object-detection","title":"Soft Anchor-Point Object Detection","date":"2019-11-27","arxiv_id":"1911.12448","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/soft-anchor-point-object-detection#ran","syntology_url":"https://syntology.ai/paper/1911.12448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12448"}},"official":null}},{"url":"/paper/learning-modulated-loss-for-rotated-object","slug":"learning-modulated-loss-for-rotated-object","title":"Learning Modulated Loss for Rotated Object Detection","date":"2019-11-19","arxiv_id":"1911.08299","repositories_listed":2,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/learning-modulated-loss-for-rotated-object#ran","syntology_url":"https://syntology.ai/paper/1911.08299","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08299"}},"official":null}},{"url":"/paper/openloris-object-a-dataset-and-benchmark","slug":"openloris-object-a-dataset-and-benchmark","title":"OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning","date":"2019-11-15","arxiv_id":"1911.06487","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/openloris-object-a-dataset-and-benchmark#ran","syntology_url":"https://syntology.ai/paper/1911.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06487"}},"official":null}},{"url":"/paper/making-an-invisibility-cloak-real-world","slug":"making-an-invisibility-cloak-real-world","title":"Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors","date":"2019-10-31","arxiv_id":"1910.14667","repositories_listed":2,"syntology":null},{"url":"/paper/191013439","slug":"191013439","title":"Learning to Manipulate Deformable Objects without Demonstrations","date":"2019-10-29","arxiv_id":"1910.13439","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/191013439#ran","syntology_url":"https://syntology.ai/paper/1910.13439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13439"}},"official":{"repos":["wilson1yan/rlpyt"],"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/deep-image-blending","slug":"deep-image-blending","title":"Deep Image Blending","date":"2019-10-25","arxiv_id":"1910.11495","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/deep-image-blending#ran","syntology_url":"https://syntology.ai/paper/1910.11495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11495"}},"official":{"repos":["owenzlz/DeepImageBlending","owenzlz/Deep_Image_Blending"],"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/6-pack-category-level-6d-pose-tracker-with","slug":"6-pack-category-level-6d-pose-tracker-with","title":"6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints","date":"2019-10-23","arxiv_id":"1910.10750","repositories_listed":2,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/6-pack-category-level-6d-pose-tracker-with#ran","syntology_url":"https://syntology.ai/paper/1910.10750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10750"}},"official":{"repos":["j96w/6-PACK"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/reinforcement-learning-for-robotic","slug":"reinforcement-learning-for-robotic","title":"Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations","date":"2019-10-16","arxiv_id":"1910.07294","repositories_listed":2,"syntology":null},{"url":"/paper/cater-a-diagnostic-dataset-for-compositional","slug":"cater-a-diagnostic-dataset-for-compositional","title":"CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning","date":"2019-10-10","arxiv_id":"1910.04744","repositories_listed":2,"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":0,"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/cater-a-diagnostic-dataset-for-compositional#ran","syntology_url":"https://syntology.ai/paper/1910.04744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04744"}},"official":null}},{"url":"/paper/deformable-kernels-adapting-effective","slug":"deformable-kernels-adapting-effective","title":"Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation","date":"2019-10-07","arxiv_id":"1910.02940","repositories_listed":2,"syntology":null},{"url":"/paper/scalable-object-oriented-sequential-1","slug":"scalable-object-oriented-sequential-1","title":"SCALOR: Generative World Models with Scalable Object Representations","date":"2019-10-06","arxiv_id":"1910.02384","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scalable-object-oriented-sequential-1#ran","syntology_url":"https://syntology.ai/paper/1910.02384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02384"}},"official":null}},{"url":"/paper/compensating-supervision-incompleteness-with","slug":"compensating-supervision-incompleteness-with","title":"Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation","date":"2019-10-01","arxiv_id":"1910.00462","repositories_listed":2,"syntology":null},{"url":"/paper/integral-object-mining-via-online-attention","slug":"integral-object-mining-via-online-attention","title":"Integral Object Mining via Online Attention Accumulation","date":"2019-10-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/fast-and-accurate-convolutional-object","slug":"fast-and-accurate-convolutional-object","title":"Development of Fast Refinement Detectors on AI Edge Platforms","date":"2019-09-24","arxiv_id":"1909.10798","repositories_listed":2,"syntology":null},{"url":"/paper/190909709","slug":"190909709","title":"SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems","date":"2019-09-20","arxiv_id":"1909.09709","repositories_listed":2,"syntology":null},{"url":"/paper/motion-guided-attention-for-video-salient","slug":"motion-guided-attention-for-video-salient","title":"Motion Guided Attention for Video Salient Object Detection","date":"2019-09-16","arxiv_id":"1909.07061","repositories_listed":2,"syntology":null},{"url":"/paper/gradnet-gradient-guided-network-for-visual","slug":"gradnet-gradient-guided-network-for-visual","title":"GradNet: Gradient-Guided Network for Visual Object Tracking","date":"2019-09-15","arxiv_id":"1909.06800","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/gradnet-gradient-guided-network-for-visual#ran","syntology_url":"https://syntology.ai/paper/1909.06800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06800"}},"official":{"repos":["LPXTT/GradNet-Pytorch","LPXTT/GradNet-Tensorflow"],"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/specifying-object-attributes-and-relations-in","slug":"specifying-object-attributes-and-relations-in","title":"Specifying Object Attributes and Relations in Interactive Scene Generation","date":"2019-09-11","arxiv_id":"1909.05379","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":3,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/specifying-object-attributes-and-relations-in#ran","syntology_url":"https://syntology.ai/paper/1909.05379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05379"}},"official":{"repos":["ashual/scene_generation"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/upc-learning-universal-physical-camouflage","slug":"upc-learning-universal-physical-camouflage","title":"Universal Physical Camouflage Attacks on Object Detectors","date":"2019-09-10","arxiv_id":"1909.04326","repositories_listed":2,"syntology":null},{"url":"/paper/relation-distillation-networks-for-video","slug":"relation-distillation-networks-for-video","title":"Relation Distillation Networks for Video Object Detection","date":"2019-08-26","arxiv_id":"1908.09511","repositories_listed":2,"syntology":null},{"url":"/paper/ranet-ranking-attention-network-for-fast","slug":"ranet-ranking-attention-network-for-fast","title":"RANet: Ranking Attention Network for Fast Video Object Segmentation","date":"2019-08-19","arxiv_id":"1908.06647","repositories_listed":2,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"11 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ranet-ranking-attention-network-for-fast#ran","syntology_url":"https://syntology.ai/paper/1908.06647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06647"}},"official":{"repos":["Storife/RANet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/matrix-nets-a-new-deep-architecture-for","slug":"matrix-nets-a-new-deep-architecture-for","title":"Matrix Nets: A New Deep Architecture for Object Detection","date":"2019-08-13","arxiv_id":"1908.04646","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-based-quantification-of","slug":"deep-learning-based-quantification-of","title":"Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides","date":"2019-08-12","arxiv_id":"1908.04767","repositories_listed":2,"syntology":null},{"url":"/paper/delving-into-robust-object-detection-from","slug":"delving-into-robust-object-detection-from","title":"Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach","date":"2019-08-11","arxiv_id":"1908.03856","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/delving-into-robust-object-detection-from#ran","syntology_url":"https://syntology.ai/paper/1908.03856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.03856"}},"official":{"repos":["TAMU-VITA/UAV-NDFT","VITA-Group/UAV-NDFT"],"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/slimyolov3-narrower-faster-and-better-for","slug":"slimyolov3-narrower-faster-and-better-for","title":"SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications","date":"2019-07-25","arxiv_id":"1907.11093","repositories_listed":2,"syntology":null},{"url":"/paper/tracking-holistic-object-representations","slug":"tracking-holistic-object-representations","title":"Tracking Holistic Object Representations","date":"2019-07-21","arxiv_id":"1907.12920","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-rgb-d-salient-object-detection","slug":"rethinking-rgb-d-salient-object-detection","title":"Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks","date":"2019-07-15","arxiv_id":"1907.06781","repositories_listed":2,"syntology":null},{"url":"/paper/sequence-level-semantics-aggregation-for","slug":"sequence-level-semantics-aggregation-for","title":"Sequence Level Semantics Aggregation for Video Object Detection","date":"2019-07-15","arxiv_id":"1907.06390","repositories_listed":2,"syntology":null},{"url":"/paper/language-as-an-abstraction-for-hierarchical","slug":"language-as-an-abstraction-for-hierarchical","title":"Language as an Abstraction for Hierarchical Deep Reinforcement Learning","date":"2019-06-18","arxiv_id":"1906.07343","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/language-as-an-abstraction-for-hierarchical#ran","syntology_url":"https://syntology.ai/paper/1906.07343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.07343"}},"official":{"repos":["google-research/clevr_robot_env"],"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/deepmot-a-differentiable-framework-for","slug":"deepmot-a-differentiable-framework-for","title":"How To Train Your Deep Multi-Object Tracker","date":"2019-06-15","arxiv_id":"1906.06618","repositories_listed":2,"syntology":null},{"url":"/paper/pose-from-shape-deep-pose-estimation-for","slug":"pose-from-shape-deep-pose-estimation-for","title":"Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects","date":"2019-06-12","arxiv_id":"1906.05105","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pose-from-shape-deep-pose-estimation-for#ran","syntology_url":"https://syntology.ai/paper/1906.05105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.05105"}},"official":null}},{"url":"/paper/learning-roi-transformer-for-oriented-object","slug":"learning-roi-transformer-for-oriented-object","title":"Learning RoI Transformer for Oriented Object Detection in Aerial Images","date":"2019-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/6-dof-graspnet-variational-grasp-generation","slug":"6-dof-graspnet-variational-grasp-generation","title":"6-DOF GraspNet: Variational Grasp Generation for Object Manipulation","date":"2019-05-25","arxiv_id":"1905.10520","repositories_listed":2,"syntology":{"n":22,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":22,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/6-dof-graspnet-variational-grasp-generation#ran","syntology_url":"https://syntology.ai/paper/1905.10520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10520"}},"official":null}},{"url":"/paper/cobra-data-efficient-model-based-rl-through","slug":"cobra-data-efficient-model-based-rl-through","title":"COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration","date":"2019-05-22","arxiv_id":"1905.09275","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/cobra-data-efficient-model-based-rl-through#ran","syntology_url":"https://syntology.ai/paper/1905.09275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09275"}},"official":{"repos":["deepmind/spriteworld"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-3d-object-detection-from-simulated","slug":"multimodal-3d-object-detection-from-simulated","title":"Multimodal 3D Object Detection from Simulated Pretraining","date":"2019-05-19","arxiv_id":"1905.07754","repositories_listed":2,"syntology":null},{"url":"/paper/object-contour-and-edge-detection-with","slug":"object-contour-and-edge-detection-with","title":"Object Contour and Edge Detection with RefineContourNet","date":"2019-04-30","arxiv_id":"1904.13353","repositories_listed":2,"syntology":null},{"url":"/paper/the-neuro-symbolic-concept-learner-1","slug":"the-neuro-symbolic-concept-learner-1","title":"The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision","date":"2019-04-26","arxiv_id":"1904.12584","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/the-neuro-symbolic-concept-learner-1#ran","syntology_url":"https://syntology.ai/paper/1904.12584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12584"}},"official":{"repos":["vacancy/NSCL-PyTorch-Release"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-object-detection-in-compressed-jpeg","slug":"fast-object-detection-in-compressed-jpeg","title":"Fast object detection in compressed JPEG Images","date":"2019-04-16","arxiv_id":"1904.08408","repositories_listed":2,"syntology":null},{"url":"/paper/contactdb-analyzing-and-predicting-grasp","slug":"contactdb-analyzing-and-predicting-grasp","title":"ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging","date":"2019-04-15","arxiv_id":"1904.06830","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-classification-and-localization-in","slug":"rethinking-classification-and-localization-in","title":"Rethinking Classification and Localization for Object Detection","date":"2019-04-13","arxiv_id":"1904.06493","repositories_listed":2,"syntology":null}],"record_sha256":"fe3a9c60d1ffd428ca0da57b438637fce8153e8fa330cb5abed1fac170c6ec10","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}