{"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/ran/10","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":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":10,"pages_in_order":11,"rows_per_page":100,"rows":[901,1000],"of":1043,"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/papers/ran/1","prev":"/task/object/papers/ran/9","next":"/task/object/papers/ran/11","papers":[{"url":"/paper/distilling-object-detectors-with-fine-grained-1","slug":"distilling-object-detectors-with-fine-grained-1","title":"Distilling Object Detectors with Fine-grained Feature Imitation","date":"2019-06-09","arxiv_id":"1906.03609","repositories_listed":3,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/distilling-object-detectors-with-fine-grained-1#ran","syntology_url":"https://syntology.ai/paper/1906.03609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03609"}},"official":{"repos":["twangnh/Distilling-Object-Detectors"],"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"]}}},{"url":"/paper/triangulation-learning-network-from-monocular-1","slug":"triangulation-learning-network-from-monocular-1","title":"Triangulation Learning Network: from Monocular to Stereo 3D Object Detection","date":"2019-06-04","arxiv_id":"1906.01193","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/triangulation-learning-network-from-monocular-1#ran","syntology_url":"https://syntology.ai/paper/1906.01193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01193"}},"official":null}},{"url":"/paper/isaid-a-large-scale-dataset-for-instance","slug":"isaid-a-large-scale-dataset-for-instance","title":"iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images","date":"2019-05-30","arxiv_id":"1905.12886","repositories_listed":3,"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":1,"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/isaid-a-large-scale-dataset-for-instance#ran","syntology_url":"https://syntology.ai/paper/1905.12886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.12886"}},"official":{"repos":["CAPTAIN-WHU/iSAID_Devkit"],"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/object-discovery-with-a-copy-pasting-gan","slug":"object-discovery-with-a-copy-pasting-gan","title":"Object Discovery with a Copy-Pasting GAN","date":"2019-05-27","arxiv_id":"1905.11369","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/object-discovery-with-a-copy-pasting-gan#ran","syntology_url":"https://syntology.ai/paper/1905.11369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11369"}},"official":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/uncertainty-estimation-in-one-stage-object","slug":"uncertainty-estimation-in-one-stage-object","title":"Uncertainty Estimation in One-Stage Object Detection","date":"2019-05-24","arxiv_id":"1905.10296","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uncertainty-estimation-in-one-stage-object#ran","syntology_url":"https://syntology.ai/paper/1905.10296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10296"}},"official":{"repos":["flkraus/bayesian-yolov3"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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/gmnn-graph-markov-neural-networks","slug":"gmnn-graph-markov-neural-networks","title":"GMNN: Graph Markov Neural Networks","date":"2019-05-15","arxiv_id":"1905.06214","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/gmnn-graph-markov-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1905.06214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06214"}},"official":null}},{"url":"/paper/object-detection-in-20-years-a-survey","slug":"object-detection-in-20-years-a-survey","title":"Object Detection in 20 Years: A Survey","date":"2019-05-13","arxiv_id":"1905.05055","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/object-detection-in-20-years-a-survey#ran","syntology_url":"https://syntology.ai/paper/1905.05055","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.05055"}},"official":null}},{"url":"/paper/language-conditioned-graph-networks-for","slug":"language-conditioned-graph-networks-for","title":"Language-Conditioned Graph Networks for Relational Reasoning","date":"2019-05-10","arxiv_id":"1905.04405","repositories_listed":1,"syntology":{"n":10,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/language-conditioned-graph-networks-for#ran","syntology_url":"https://syntology.ai/paper/1905.04405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.04405"}},"official":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/reppoints-point-set-representation-for-object","slug":"reppoints-point-set-representation-for-object","title":"RepPoints: Point Set Representation for Object Detection","date":"2019-04-25","arxiv_id":"1904.11490","repositories_listed":6,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reppoints-point-set-representation-for-object#ran","syntology_url":"https://syntology.ai/paper/1904.11490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.11490"}},"official":{"repos":["microsoft/RepPoints"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-hough-voting-for-3d-object-detection-in","slug":"deep-hough-voting-for-3d-object-detection-in","title":"Deep Hough Voting for 3D Object Detection in Point Clouds","date":"2019-04-21","arxiv_id":"1904.09664","repositories_listed":13,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-hough-voting-for-3d-object-detection-in#ran","syntology_url":"https://syntology.ai/paper/1904.09664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09664"}},"official":{"repos":["facebookresearch/votenet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/190408900","slug":"190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","arxiv_id":"1904.08900","repositories_listed":6,"syntology":{"n":27,"n_ran":21,"n_constructed":0,"n_ran_checked":21,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":20,"n_pointer_only":0,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 1 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/190408900#ran","syntology_url":"https://syntology.ai/paper/1904.08900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08900"}},"official":{"repos":["princeton-vl/CornerNet-Lite"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/centernet-object-detection-with-keypoint","slug":"centernet-object-detection-with-keypoint","title":"CenterNet: Keypoint Triplets for Object Detection","date":"2019-04-17","arxiv_id":"1904.08189","repositories_listed":20,"syntology":{"n":11,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/centernet-object-detection-with-keypoint#ran","syntology_url":"https://syntology.ai/paper/1904.08189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08189"}},"official":{"repos":["Duankaiwen/CenterNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/object-oriented-dynamics-learning-through","slug":"object-oriented-dynamics-learning-through","title":"Object-Oriented Dynamics Learning through Multi-Level Abstraction","date":"2019-04-16","arxiv_id":"1904.07482","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/object-oriented-dynamics-learning-through#ran","syntology_url":"https://syntology.ai/paper/1904.07482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.07482"}},"official":null}},{"url":"/paper/objects-as-points","slug":"objects-as-points","title":"Objects as Points","date":"2019-04-16","arxiv_id":"1904.07850","repositories_listed":76,"syntology":{"n":130,"n_ran":77,"n_constructed":0,"n_ran_checked":69,"n_instrument":8,"n_unverified":53,"n_honours":0,"n_violates":0,"n_no_contract":69,"n_pointer_only":2,"phrase":"77 ran (of which 0 constructed an object rather than computing a result; 69 with no instrument failure: 0 honoured, 0 violated, 69 with no contract checked; 8 where Syntology's instrument failed) · 53 unverified","sample_list":"/paper/objects-as-points#ran","syntology_url":"https://syntology.ai/paper/1904.07850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.07850"}},"official":{"repos":["xingyizhou/CenterNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["listed"]}}},{"url":"/paper/clustered-object-detection-in-aerial-images","slug":"clustered-object-detection-in-aerial-images","title":"Clustered Object Detection in Aerial Images","date":"2019-04-16","arxiv_id":"1904.08008","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/clustered-object-detection-in-aerial-images#ran","syntology_url":"https://syntology.ai/paper/1904.08008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08008"}},"official":null}},{"url":"/paper/what-object-should-i-use-task-driven-object","slug":"what-object-should-i-use-task-driven-object","title":"What Object Should I Use? - Task Driven Object Detection","date":"2019-04-05","arxiv_id":"1904.03000","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/what-object-should-i-use-task-driven-object#ran","syntology_url":"https://syntology.ai/paper/1904.03000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03000"}},"official":null}},{"url":"/paper/spatiotemporal-cnn-for-video-object","slug":"spatiotemporal-cnn-for-video-object","title":"Spatiotemporal CNN for Video Object Segmentation","date":"2019-04-04","arxiv_id":"1904.02363","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":1,"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/spatiotemporal-cnn-for-video-object#ran","syntology_url":"https://syntology.ai/paper/1904.02363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02363"}},"official":{"repos":["longyin880815/STCNN"],"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/fcos-fully-convolutional-one-stage-object","slug":"fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","arxiv_id":"1904.01355","repositories_listed":87,"syntology":{"n":40,"n_ran":37,"n_constructed":0,"n_ran_checked":31,"n_instrument":6,"n_unverified":3,"n_honours":2,"n_violates":2,"n_no_contract":27,"n_pointer_only":19,"phrase":"37 ran (of which 0 constructed an object rather than computing a result; 31 with no instrument failure: 2 honoured, 2 violated, 27 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fcos-fully-convolutional-one-stage-object#ran","syntology_url":"https://syntology.ai/paper/1904.01355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01355"}},"official":{"repos":["tianzhi0549/FCOS"],"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/monocular-3d-object-detection-leveraging","slug":"monocular-3d-object-detection-leveraging","title":"Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction","date":"2019-04-02","arxiv_id":"1904.01690","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/monocular-3d-object-detection-leveraging#ran","syntology_url":"https://syntology.ai/paper/1904.01690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01690"}},"official":null}},{"url":"/paper/video-object-segmentation-using-space-time","slug":"video-object-segmentation-using-space-time","title":"Video Object Segmentation using Space-Time Memory Networks","date":"2019-04-01","arxiv_id":"1904.00607","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/video-object-segmentation-using-space-time#ran","syntology_url":"https://syntology.ai/paper/1904.00607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00607"}},"official":null}},{"url":"/paper/detnas-neural-architecture-search-on-object","slug":"detnas-neural-architecture-search-on-object","title":"DetNAS: Backbone Search for Object Detection","date":"2019-03-26","arxiv_id":"1903.10979","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/detnas-neural-architecture-search-on-object#ran","syntology_url":"https://syntology.ai/paper/1903.10979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.10979"}},"official":{"repos":["megvii-model/DetNAS"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/photometric-mesh-optimization-for-video","slug":"photometric-mesh-optimization-for-video","title":"Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction","date":"2019-03-20","arxiv_id":"1903.08642","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/photometric-mesh-optimization-for-video#ran","syntology_url":"https://syntology.ai/paper/1903.08642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.08642"}},"official":{"repos":["chenhsuanlin/photometric-mesh-optim"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/tracking-without-bells-and-whistles","slug":"tracking-without-bells-and-whistles","title":"Tracking without bells and whistles","date":"2019-03-13","arxiv_id":"1903.05625","repositories_listed":13,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/tracking-without-bells-and-whistles#ran","syntology_url":"https://syntology.ai/paper/1903.05625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.05625"}},"official":{"repos":["phil-bergmann/tracking_wo_bnw"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/bayesod-a-bayesian-approach-for-uncertainty","slug":"bayesod-a-bayesian-approach-for-uncertainty","title":"BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors","date":"2019-03-09","arxiv_id":"1903.03838","repositories_listed":2,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/bayesod-a-bayesian-approach-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/1903.03838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03838"}},"official":{"repos":["asharakeh/bayes-od-rc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-complementary-parts-models","slug":"weakly-supervised-complementary-parts-models","title":"Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up","date":"2019-03-07","arxiv_id":"1903.02827","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/weakly-supervised-complementary-parts-models#ran","syntology_url":"https://syntology.ai/paper/1903.02827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02827"}},"official":null}},{"url":"/paper/multi-object-representation-learning-with","slug":"multi-object-representation-learning-with","title":"Multi-Object Representation Learning with Iterative Variational Inference","date":"2019-03-01","arxiv_id":"1903.00450","repositories_listed":6,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-object-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/1903.00450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00450"}},"official":{"repos":["deepmind/deepmind-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/dpod-dense-6d-pose-object-detector-in-rgb","slug":"dpod-dense-6d-pose-object-detector-in-rgb","title":"DPOD: 6D Pose Object Detector and Refiner","date":"2019-02-28","arxiv_id":"1902.11020","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dpod-dense-6d-pose-object-detector-in-rgb#ran","syntology_url":"https://syntology.ai/paper/1902.11020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.11020"}},"official":{"repos":["zakharos/DPOD"],"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/stereo-r-cnn-based-3d-object-detection-for","slug":"stereo-r-cnn-based-3d-object-detection-for","title":"Stereo R-CNN based 3D Object Detection for Autonomous Driving","date":"2019-02-26","arxiv_id":"1902.09738","repositories_listed":4,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/stereo-r-cnn-based-3d-object-detection-for#ran","syntology_url":"https://syntology.ai/paper/1902.09738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09738"}},"official":{"repos":["HKUST-Aerial-Robotics/Stereo-RCNN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/feelvos-fast-end-to-end-embedding-learning","slug":"feelvos-fast-end-to-end-embedding-learning","title":"FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation","date":"2019-02-25","arxiv_id":"1902.09513","repositories_listed":3,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/feelvos-fast-end-to-end-embedding-learning#ran","syntology_url":"https://syntology.ai/paper/1902.09513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09513"}},"official":{"repos":["tensorflow/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/predictive-inequity-in-object-detection","slug":"predictive-inequity-in-object-detection","title":"Predictive Inequity in Object Detection","date":"2019-02-21","arxiv_id":"1902.11097","repositories_listed":1,"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/predictive-inequity-in-object-detection#ran","syntology_url":"https://syntology.ai/paper/1902.11097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.11097"}},"official":null}},{"url":"/paper/pixor-real-time-3d-object-detection-from","slug":"pixor-real-time-3d-object-detection-from","title":"PIXOR: Real-time 3D Object Detection from Point Clouds","date":"2019-02-17","arxiv_id":"1902.06326","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pixor-real-time-3d-object-detection-from#ran","syntology_url":"https://syntology.ai/paper/1902.06326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06326"}},"official":null}},{"url":"/paper/multigrain-a-unified-image-embedding-for","slug":"multigrain-a-unified-image-embedding-for","title":"MultiGrain: a unified image embedding for classes and instances","date":"2019-02-14","arxiv_id":"1902.05509","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/multigrain-a-unified-image-embedding-for#ran","syntology_url":"https://syntology.ai/paper/1902.05509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.05509"}},"official":{"repos":["facebookresearch/multigrain"],"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/stampnet-unsupervised-multi-class-object","slug":"stampnet-unsupervised-multi-class-object","title":"StampNet: unsupervised multi-class object discovery","date":"2019-02-07","arxiv_id":"1902.02693","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/stampnet-unsupervised-multi-class-object#ran","syntology_url":"https://syntology.ai/paper/1902.02693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.02693"}},"official":null}},{"url":"/paper/implicit-3d-orientation-learning-for-6d","slug":"implicit-3d-orientation-learning-for-6d","title":"Implicit 3D Orientation Learning for 6D Object Detection from RGB Images","date":"2019-02-04","arxiv_id":"1902.01275","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/implicit-3d-orientation-learning-for-6d#ran","syntology_url":"https://syntology.ai/paper/1902.01275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01275"}},"official":{"repos":["DLR-RM/AugmentedAutoencoder"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/4d-generic-video-object-proposals","slug":"4d-generic-video-object-proposals","title":"4D Generic Video Object Proposals","date":"2019-01-26","arxiv_id":"1901.09260","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/4d-generic-video-object-proposals#ran","syntology_url":"https://syntology.ai/paper/1901.09260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09260"}},"official":{"repos":["aljosaosep/4DGVT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bottom-up-object-detection-by-grouping","slug":"bottom-up-object-detection-by-grouping","title":"Bottom-up Object Detection by Grouping Extreme and Center Points","date":"2019-01-23","arxiv_id":"1901.08043","repositories_listed":2,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/bottom-up-object-detection-by-grouping#ran","syntology_url":"https://syntology.ai/paper/1901.08043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08043"}},"official":{"repos":["xingyizhou/ExtremeNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/densefusion-6d-object-pose-estimation-by","slug":"densefusion-6d-object-pose-estimation-by","title":"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion","date":"2019-01-15","arxiv_id":"1901.04780","repositories_listed":8,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/densefusion-6d-object-pose-estimation-by#ran","syntology_url":"https://syntology.ai/paper/1901.04780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.04780"}},"official":null}},{"url":"/paper/unsupervised-moving-object-detection-via","slug":"unsupervised-moving-object-detection-via","title":"Unsupervised Moving Object Detection via Contextual Information Separation","date":"2019-01-10","arxiv_id":"1901.03360","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/unsupervised-moving-object-detection-via#ran","syntology_url":"https://syntology.ai/paper/1901.03360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.03360"}},"official":null}},{"url":"/paper/normalized-object-coordinate-space-for","slug":"normalized-object-coordinate-space-for","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","date":"2019-01-09","arxiv_id":"1901.02970","repositories_listed":10,"syntology":{"n":20,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/normalized-object-coordinate-space-for#ran","syntology_url":"https://syntology.ai/paper/1901.02970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02970"}},"official":{"repos":["hughw19/NOCS_CVPR2019"],"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/nocaps-novel-object-captioning-at-scale","slug":"nocaps-novel-object-captioning-at-scale","title":"nocaps: novel object captioning at scale","date":"2018-12-20","arxiv_id":"1812.08658","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/nocaps-novel-object-captioning-at-scale#ran","syntology_url":"https://syntology.ai/paper/1812.08658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.08658"}},"official":{"repos":["nocaps-org/updown-baseline"],"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/pointpillars-fast-encoders-for-object","slug":"pointpillars-fast-encoders-for-object","title":"PointPillars: Fast Encoders for Object Detection from Point Clouds","date":"2018-12-14","arxiv_id":"1812.05784","repositories_listed":18,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pointpillars-fast-encoders-for-object#ran","syntology_url":"https://syntology.ai/paper/1812.05784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.05784"}},"official":{"repos":["nutonomy/second.pytorch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/fast-online-object-tracking-and-segmentation","slug":"fast-online-object-tracking-and-segmentation","title":"Fast Online Object Tracking and Segmentation: A Unifying Approach","date":"2018-12-12","arxiv_id":"1812.05050","repositories_listed":3,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fast-online-object-tracking-and-segmentation#ran","syntology_url":"https://syntology.ai/paper/1812.05050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.05050"}},"official":null}},{"url":"/paper/gspn-generative-shape-proposal-network-for-3d","slug":"gspn-generative-shape-proposal-network-for-3d","title":"GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud","date":"2018-12-08","arxiv_id":"1812.03320","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gspn-generative-shape-proposal-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/1812.03320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.03320"}},"official":{"repos":["ericyi/GSPN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/segmentation-driven-6d-object-pose-estimation","slug":"segmentation-driven-6d-object-pose-estimation","title":"Segmentation-driven 6D Object Pose Estimation","date":"2018-12-06","arxiv_id":"1812.02541","repositories_listed":5,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/segmentation-driven-6d-object-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/1812.02541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.02541"}},"official":{"repos":["cvlab-epfl/segmentation-driven-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/few-shot-object-detection-via-feature","slug":"few-shot-object-detection-via-feature","title":"Few-shot Object Detection via Feature Reweighting","date":"2018-12-05","arxiv_id":"1812.01866","repositories_listed":4,"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/few-shot-object-detection-via-feature#ran","syntology_url":"https://syntology.ai/paper/1812.01866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01866"}},"official":{"repos":["bingykang/Fewshot_Detection"],"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/learning-roi-transformer-for-detecting","slug":"learning-roi-transformer-for-detecting","title":"Learning RoI Transformer for Detecting Oriented Objects in Aerial Images","date":"2018-12-01","arxiv_id":"1812.00155","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"10 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-roi-transformer-for-detecting#ran","syntology_url":"https://syntology.ai/paper/1812.00155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00155"}},"official":null}},{"url":"/paper/transferable-adversarial-attacks-for-image","slug":"transferable-adversarial-attacks-for-image","title":"Transferable Adversarial Attacks for Image and Video Object Detection","date":"2018-11-30","arxiv_id":"1811.12641","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/transferable-adversarial-attacks-for-image#ran","syntology_url":"https://syntology.ai/paper/1811.12641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12641"}},"official":null}},{"url":"/paper/grid-r-cnn","slug":"grid-r-cnn","title":"Grid R-CNN","date":"2018-11-29","arxiv_id":"1811.12030","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/grid-r-cnn#ran","syntology_url":"https://syntology.ai/paper/1811.12030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12030"}},"official":null}},{"url":"/paper/espnetv2-a-light-weight-power-efficient-and","slug":"espnetv2-a-light-weight-power-efficient-and","title":"ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network","date":"2018-11-28","arxiv_id":"1811.11431","repositories_listed":10,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/espnetv2-a-light-weight-power-efficient-and#ran","syntology_url":"https://syntology.ai/paper/1811.11431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11431"}},"official":{"repos":["sacmehta/EdgeNets"],"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/semantic-part-detection-via-matching-learning","slug":"semantic-part-detection-via-matching-learning","title":"Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data","date":"2018-11-28","arxiv_id":"1811.11823","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-part-detection-via-matching-learning#ran","syntology_url":"https://syntology.ai/paper/1811.11823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11823"}},"official":{"repos":["ytongbai/SemanticPartDetection"],"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/probability-based-detection-quality-pdq-a","slug":"probability-based-detection-quality-pdq-a","title":"Probabilistic Object Detection: Definition and Evaluation","date":"2018-11-27","arxiv_id":"1811.10800","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/probability-based-detection-quality-pdq-a#ran","syntology_url":"https://syntology.ai/paper/1811.10800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10800"}},"official":null}},{"url":"/paper/deformable-convnets-v2-more-deformable-better","slug":"deformable-convnets-v2-more-deformable-better","title":"Deformable ConvNets v2: More Deformable, Better Results","date":"2018-11-27","arxiv_id":"1811.11168","repositories_listed":26,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deformable-convnets-v2-more-deformable-better#ran","syntology_url":"https://syntology.ai/paper/1811.11168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11168"}},"official":null}},{"url":"/paper/gan-dissection-visualizing-and-understanding","slug":"gan-dissection-visualizing-and-understanding","title":"GAN Dissection: Visualizing and Understanding Generative Adversarial Networks","date":"2018-11-26","arxiv_id":"1811.10597","repositories_listed":8,"syntology":{"n":34,"n_ran":25,"n_constructed":0,"n_ran_checked":23,"n_instrument":2,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":23,"n_pointer_only":3,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 23 with no instrument failure: 0 honoured, 0 violated, 23 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/gan-dissection-visualizing-and-understanding#ran","syntology_url":"https://syntology.ai/paper/1811.10597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10597"}},"official":{"repos":["CSAILVision/gandissect"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/orthographic-feature-transform-for-monocular","slug":"orthographic-feature-transform-for-monocular","title":"Orthographic Feature Transform for Monocular 3D Object Detection","date":"2018-11-20","arxiv_id":"1811.08188","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"12 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/orthographic-feature-transform-for-monocular#ran","syntology_url":"https://syntology.ai/paper/1811.08188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.08188"}},"official":null}},{"url":"/paper/transferable-interactiveness-prior-for-human","slug":"transferable-interactiveness-prior-for-human","title":"Transferable Interactiveness Knowledge for Human-Object Interaction Detection","date":"2018-11-20","arxiv_id":"1811.08264","repositories_listed":3,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/transferable-interactiveness-prior-for-human#ran","syntology_url":"https://syntology.ai/paper/1811.08264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.08264"}},"official":{"repos":["DirtyHarryLYL/Transferable-Interactiveness-Network"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/grasp2vec-learning-object-representations","slug":"grasp2vec-learning-object-representations","title":"Grasp2Vec: Learning Object Representations from Self-Supervised Grasping","date":"2018-11-16","arxiv_id":"1811.06964","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/grasp2vec-learning-object-representations#ran","syntology_url":"https://syntology.ai/paper/1811.06964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06964"}},"official":null}},{"url":"/paper/m2det-a-single-shot-object-detector-based-on","slug":"m2det-a-single-shot-object-detector-based-on","title":"M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network","date":"2018-11-12","arxiv_id":"1811.04533","repositories_listed":11,"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/m2det-a-single-shot-object-detector-based-on#ran","syntology_url":"https://syntology.ai/paper/1811.04533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.04533"}},"official":null}},{"url":"/paper/cooperative-holistic-scene-understanding","slug":"cooperative-holistic-scene-understanding","title":"Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation","date":"2018-10-31","arxiv_id":"1810.13049","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"16 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cooperative-holistic-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/1810.13049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.13049"}},"official":{"repos":["thusiyuan/cooperative_scene_parsing"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/softer-nms-rethinking-bounding-box-regression","slug":"softer-nms-rethinking-bounding-box-regression","title":"Bounding Box Regression with Uncertainty for Accurate Object Detection","date":"2018-09-23","arxiv_id":"1809.08545","repositories_listed":4,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/softer-nms-rethinking-bounding-box-regression#ran","syntology_url":"https://syntology.ai/paper/1809.08545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08545"}},"official":{"repos":["yihui-he/softer-NMS","yihui-he/KL-Loss"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ican-instance-centric-attention-network-for","slug":"ican-instance-centric-attention-network-for","title":"iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection","date":"2018-08-30","arxiv_id":"1808.10437","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ican-instance-centric-attention-network-for#ran","syntology_url":"https://syntology.ai/paper/1808.10437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.10437"}},"official":{"repos":["vt-vl-lab/iCAN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-hierarchical-semantic-image","slug":"learning-hierarchical-semantic-image","title":"Learning Hierarchical Semantic Image Manipulation through Structured Representations","date":"2018-08-22","arxiv_id":"1808.07535","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/learning-hierarchical-semantic-image#ran","syntology_url":"https://syntology.ai/paper/1808.07535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.07535"}},"official":null}},{"url":"/paper/holistic-3d-scene-parsing-and-reconstruction","slug":"holistic-3d-scene-parsing-and-reconstruction","title":"Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image","date":"2018-08-07","arxiv_id":"1808.02201","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/holistic-3d-scene-parsing-and-reconstruction#ran","syntology_url":"https://syntology.ai/paper/1808.02201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.02201"}},"official":null}},{"url":"/paper/cornernet-detecting-objects-as-paired","slug":"cornernet-detecting-objects-as-paired","title":"CornerNet: Detecting Objects as Paired Keypoints","date":"2018-08-03","arxiv_id":"1808.01244","repositories_listed":5,"syntology":{"n":11,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/cornernet-detecting-objects-as-paired#ran","syntology_url":"https://syntology.ai/paper/1808.01244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.01244"}},"official":{"repos":["princeton-vl/CornerNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/acquisition-of-localization-confidence-for","slug":"acquisition-of-localization-confidence-for","title":"Acquisition of Localization Confidence for Accurate Object Detection","date":"2018-07-30","arxiv_id":"1807.11590","repositories_listed":4,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":15,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":2,"phrase":"17 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/acquisition-of-localization-confidence-for#ran","syntology_url":"https://syntology.ai/paper/1807.11590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11590"}},"official":{"repos":["vacancy/PreciseRoIPooling"],"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/where-are-the-blobs-counting-by-localization","slug":"where-are-the-blobs-counting-by-localization","title":"Where are the Blobs: Counting by Localization with Point Supervision","date":"2018-07-25","arxiv_id":"1807.09856","repositories_listed":3,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"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) · 4 unverified","sample_list":"/paper/where-are-the-blobs-counting-by-localization#ran","syntology_url":"https://syntology.ai/paper/1807.09856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09856"}},"official":null}},{"url":"/paper/premvos-proposal-generation-refinement-and","slug":"premvos-proposal-generation-refinement-and","title":"PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation","date":"2018-07-24","arxiv_id":"1807.09190","repositories_listed":5,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"12 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/premvos-proposal-generation-refinement-and#ran","syntology_url":"https://syntology.ai/paper/1807.09190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09190"}},"official":null}},{"url":"/paper/pcl-proposal-cluster-learning-for-weakly","slug":"pcl-proposal-cluster-learning-for-weakly","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","date":"2018-07-09","arxiv_id":"1807.03342","repositories_listed":4,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pcl-proposal-cluster-learning-for-weakly#ran","syntology_url":"https://syntology.ai/paper/1807.03342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03342"}},"official":{"repos":["ppengtang/oicr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/localization-recall-precision-lrp-a-new","slug":"localization-recall-precision-lrp-a-new","title":"Localization Recall Precision (LRP): A New Performance Metric for Object Detection","date":"2018-07-04","arxiv_id":"1807.01696","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/localization-recall-precision-lrp-a-new#ran","syntology_url":"https://syntology.ai/paper/1807.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.01696"}},"official":{"repos":["cancam/LRP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperspectral-image-dataset-for-benchmarking","slug":"hyperspectral-image-dataset-for-benchmarking","title":"Hyperspectral Image Dataset for Benchmarking on Salient Object Detection","date":"2018-06-29","arxiv_id":"1806.11314","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/hyperspectral-image-dataset-for-benchmarking#ran","syntology_url":"https://syntology.ai/paper/1806.11314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.11314"}},"official":{"repos":["gistairc/HS-SOD"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/unsupervised-learning-of-object-landmarks","slug":"unsupervised-learning-of-object-landmarks","title":"Unsupervised Learning of Object Landmarks through Conditional Image Generation","date":"2018-06-20","arxiv_id":"1806.07823","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-learning-of-object-landmarks#ran","syntology_url":"https://syntology.ai/paper/1806.07823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07823"}},"official":null}},{"url":"/paper/sim-to-real-reinforcement-learning-for","slug":"sim-to-real-reinforcement-learning-for","title":"Sim-to-Real Reinforcement Learning for Deformable Object Manipulation","date":"2018-06-20","arxiv_id":"1806.07851","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sim-to-real-reinforcement-learning-for#ran","syntology_url":"https://syntology.ai/paper/1806.07851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07851"}},"official":{"repos":["JanMatas/Rainbow_ddpg"],"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/repmet-representative-based-metric-learning","slug":"repmet-representative-based-metric-learning","title":"RepMet: Representative-based metric learning for classification and one-shot object detection","date":"2018-06-12","arxiv_id":"1806.04728","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/repmet-representative-based-metric-learning#ran","syntology_url":"https://syntology.ai/paper/1806.04728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04728"}},"official":null}},{"url":"/paper/adversarial-complementary-learning-for-weakly","slug":"adversarial-complementary-learning-for-weakly","title":"Adversarial Complementary Learning for Weakly Supervised Object Localization","date":"2018-04-19","arxiv_id":"1804.06962","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-complementary-learning-for-weakly#ran","syntology_url":"https://syntology.ai/paper/1804.06962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06962"}},"official":null}},{"url":"/paper/training-deep-networks-with-synthetic-data","slug":"training-deep-networks-with-synthetic-data","title":"Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization","date":"2018-04-18","arxiv_id":"1804.06516","repositories_listed":1,"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/training-deep-networks-with-synthetic-data#ran","syntology_url":"https://syntology.ai/paper/1804.06516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06516"}},"official":null}},{"url":"/paper/deep-object-co-segmentation","slug":"deep-object-co-segmentation","title":"Deep Object Co-Segmentation","date":"2018-04-17","arxiv_id":"1804.06423","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/deep-object-co-segmentation#ran","syntology_url":"https://syntology.ai/paper/1804.06423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06423"}},"official":null}},{"url":"/paper/shapeshifter-robust-physical-adversarial","slug":"shapeshifter-robust-physical-adversarial","title":"ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector","date":"2018-04-16","arxiv_id":"1804.05810","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/shapeshifter-robust-physical-adversarial#ran","syntology_url":"https://syntology.ai/paper/1804.05810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05810"}},"official":{"repos":["shangtse/robust-physical-attack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/motion-based-object-segmentation-based-on","slug":"motion-based-object-segmentation-based-on","title":"Motion-based Object Segmentation based on Dense RGB-D Scene Flow","date":"2018-04-14","arxiv_id":"1804.05195","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/motion-based-object-segmentation-based-on#ran","syntology_url":"https://syntology.ai/paper/1804.05195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05195"}},"official":null}},{"url":"/paper/unsupervised-discovery-of-object-landmarks-as","slug":"unsupervised-discovery-of-object-landmarks-as","title":"Unsupervised Discovery of Object Landmarks as Structural Representations","date":"2018-04-12","arxiv_id":"1804.04412","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-discovery-of-object-landmarks-as#ran","syntology_url":"https://syntology.ai/paper/1804.04412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.04412"}},"official":null}},{"url":"/paper/learning-descriptor-networks-for-3d-shape","slug":"learning-descriptor-networks-for-3d-shape","title":"Learning Descriptor Networks for 3D Shape Synthesis and Analysis","date":"2018-04-02","arxiv_id":"1804.00586","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/learning-descriptor-networks-for-3d-shape#ran","syntology_url":"https://syntology.ai/paper/1804.00586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00586"}},"official":{"repos":["jianwen-xie/3DDescriptorNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-free-form-deformations-for-3d-object","slug":"learning-free-form-deformations-for-3d-object","title":"Learning Free-Form Deformations for 3D Object Reconstruction","date":"2018-03-29","arxiv_id":"1803.10932","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-free-form-deformations-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/1803.10932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10932"}},"official":{"repos":["jackd/template_ffd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-baby-talk","slug":"neural-baby-talk","title":"Neural Baby Talk","date":"2018-03-27","arxiv_id":"1803.09845","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-baby-talk#ran","syntology_url":"https://syntology.ai/paper/1803.09845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.09845"}},"official":{"repos":["jiasenlu/NeuralBabyTalk"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/attributes-as-operators-factorizing-unseen","slug":"attributes-as-operators-factorizing-unseen","title":"Attributes as Operators: Factorizing Unseen Attribute-Object Compositions","date":"2018-03-27","arxiv_id":"1803.09851","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/attributes-as-operators-factorizing-unseen#ran","syntology_url":"https://syntology.ai/paper/1803.09851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.09851"}},"official":{"repos":["Tushar-N/attributes-as-operators"],"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/object-detection-for-comics-using-manga109","slug":"object-detection-for-comics-using-manga109","title":"Object Detection for Comics using Manga109 Annotations","date":"2018-03-23","arxiv_id":"1803.08670","repositories_listed":5,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/object-detection-for-comics-using-manga109#ran","syntology_url":"https://syntology.ai/paper/1803.08670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08670"}},"official":null}},{"url":"/paper/group-normalization","slug":"group-normalization","title":"Group Normalization","date":"2018-03-22","arxiv_id":"1803.08494","repositories_listed":22,"syntology":{"n":15,"n_ran":7,"n_constructed":2,"n_ran_checked":5,"n_instrument":2,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"7 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/group-normalization#ran","syntology_url":"https://syntology.ai/paper/1803.08494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08494"}},"official":{"repos":["ppwwyyxx/GroupNorm-reproduce"],"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/learning-dynamic-memory-networks-for-object","slug":"learning-dynamic-memory-networks-for-object","title":"Learning Dynamic Memory Networks for Object Tracking","date":"2018-03-20","arxiv_id":"1803.07268","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/learning-dynamic-memory-networks-for-object#ran","syntology_url":"https://syntology.ai/paper/1803.07268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07268"}},"official":{"repos":["skyoung/MemTrack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-adaptive-faster-r-cnn-for-object","slug":"domain-adaptive-faster-r-cnn-for-object","title":"Domain Adaptive Faster R-CNN for Object Detection in the Wild","date":"2018-03-08","arxiv_id":"1803.03243","repositories_listed":8,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/domain-adaptive-faster-r-cnn-for-object#ran","syntology_url":"https://syntology.ai/paper/1803.03243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.03243"}},"official":{"repos":["yuhuayc/da-faster-rcnn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/a-twofold-siamese-network-for-real-time","slug":"a-twofold-siamese-network-for-real-time","title":"A Twofold Siamese Network for Real-Time Object Tracking","date":"2018-02-24","arxiv_id":"1802.08817","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-twofold-siamese-network-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1802.08817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08817"}},"official":null}},{"url":"/paper/cascade-r-cnn-delving-into-high-quality","slug":"cascade-r-cnn-delving-into-high-quality","title":"Cascade R-CNN: Delving into High Quality Object Detection","date":"2017-12-03","arxiv_id":"1712.00726","repositories_listed":8,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cascade-r-cnn-delving-into-high-quality#ran","syntology_url":"https://syntology.ai/paper/1712.00726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.00726"}},"official":{"repos":["zhaoweicai/cascade-rcnn"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/high-resolution-image-synthesis-and-semantic","slug":"high-resolution-image-synthesis-and-semantic","title":"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs","date":"2017-11-30","arxiv_id":"1711.11585","repositories_listed":21,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/high-resolution-image-synthesis-and-semantic#ran","syntology_url":"https://syntology.ai/paper/1711.11585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.11585"}},"official":{"repos":["NVIDIA/pix2pixHD"],"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/dota-a-large-scale-dataset-for-object","slug":"dota-a-large-scale-dataset-for-object","title":"DOTA: A Large-scale Dataset for Object Detection in Aerial Images","date":"2017-11-28","arxiv_id":"1711.10398","repositories_listed":6,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dota-a-large-scale-dataset-for-object#ran","syntology_url":"https://syntology.ai/paper/1711.10398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10398"}},"official":null}},{"url":"/paper/frustum-pointnets-for-3d-object-detection","slug":"frustum-pointnets-for-3d-object-detection","title":"Frustum PointNets for 3D Object Detection from RGB-D Data","date":"2017-11-22","arxiv_id":"1711.08488","repositories_listed":68,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/frustum-pointnets-for-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/1711.08488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08488"}},"official":{"repos":["charlesq34/frustum-pointnets"],"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/megdet-a-large-mini-batch-object-detector","slug":"megdet-a-large-mini-batch-object-detector","title":"MegDet: A Large Mini-Batch Object Detector","date":"2017-11-20","arxiv_id":"1711.07240","repositories_listed":6,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/megdet-a-large-mini-batch-object-detector#ran","syntology_url":"https://syntology.ai/paper/1711.07240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.07240"}},"official":null}},{"url":"/paper/mobile-video-object-detection-with-temporally","slug":"mobile-video-object-detection-with-temporally","title":"Mobile Video Object Detection with Temporally-Aware Feature Maps","date":"2017-11-17","arxiv_id":"1711.06368","repositories_listed":3,"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/mobile-video-object-detection-with-temporally#ran","syntology_url":"https://syntology.ai/paper/1711.06368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06368"}},"official":null}},{"url":"/paper/neural-motifs-scene-graph-parsing-with-global","slug":"neural-motifs-scene-graph-parsing-with-global","title":"Neural Motifs: Scene Graph Parsing with Global Context","date":"2017-11-17","arxiv_id":"1711.06640","repositories_listed":7,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-motifs-scene-graph-parsing-with-global#ran","syntology_url":"https://syntology.ai/paper/1711.06640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06640"}},"official":{"repos":["rowanz/neural-motifs"],"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/progressive-representation-adaptation-for","slug":"progressive-representation-adaptation-for","title":"Progressive Representation Adaptation for Weakly Supervised Object Localization","date":"2017-10-12","arxiv_id":"1710.04647","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":1,"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/progressive-representation-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/1710.04647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04647"}},"official":{"repos":["jbhuang0604/WSL"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/detect-to-track-and-track-to-detect","slug":"detect-to-track-and-track-to-detect","title":"Detect to Track and Track to Detect","date":"2017-10-11","arxiv_id":"1710.03958","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/detect-to-track-and-track-to-detect#ran","syntology_url":"https://syntology.ai/paper/1710.03958","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.03958"}},"official":{"repos":["feichtenhofer/detect-track"],"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/interpretable-convolutional-neural-networks-2","slug":"interpretable-convolutional-neural-networks-2","title":"Interpretable Convolutional Neural Networks","date":"2017-10-02","arxiv_id":"1710.00935","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/interpretable-convolutional-neural-networks-2#ran","syntology_url":"https://syntology.ai/paper/1710.00935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.00935"}},"official":{"repos":["zqs1022/interpretableCNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"07583bb1356a3809bcb8bc351bf1d80286bc61a4f48b702fd7cb7ee51f968b95","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}