{"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/11","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":11,"pages_in_order":11,"rows_per_page":100,"rows":[1001,1043],"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/10","next":null,"papers":[{"url":"/paper/incremental-learning-of-object-detectors","slug":"incremental-learning-of-object-detectors","title":"Incremental Learning of Object Detectors without Catastrophic Forgetting","date":"2017-08-23","arxiv_id":"1708.06977","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/incremental-learning-of-object-detectors#ran","syntology_url":"https://syntology.ai/paper/1708.06977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.06977"}},"official":null}},{"url":"/paper/focal-loss-for-dense-object-detection","slug":"focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","arxiv_id":"1708.02002","repositories_listed":234,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":2,"n_instrument":9,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":6,"phrase":"11 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; 9 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/focal-loss-for-dense-object-detection#ran","syntology_url":"https://syntology.ai/paper/1708.02002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.02002"}},"official":{"repos":["facebookresearch/detectron"],"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/comparing-deep-neural-networks-against-humans","slug":"comparing-deep-neural-networks-against-humans","title":"Comparing deep neural networks against humans: object recognition when the signal gets weaker","date":"2017-06-21","arxiv_id":"1706.06969","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/comparing-deep-neural-networks-against-humans#ran","syntology_url":"https://syntology.ai/paper/1706.06969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.06969"}},"official":{"repos":["rgeirhos/object-recognition"],"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/soft-nms-improving-object-detection-with-one","slug":"soft-nms-improving-object-detection-with-one","title":"Soft-NMS -- Improving Object Detection With One Line of Code","date":"2017-04-14","arxiv_id":"1704.04503","repositories_listed":8,"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":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/soft-nms-improving-object-detection-with-one#ran","syntology_url":"https://syntology.ai/paper/1704.04503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.04503"}},"official":null}},{"url":"/paper/first-person-hand-action-benchmark-with-rgb-d","slug":"first-person-hand-action-benchmark-with-rgb-d","title":"First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations","date":"2017-04-08","arxiv_id":"1704.02463","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/first-person-hand-action-benchmark-with-rgb-d#ran","syntology_url":"https://syntology.ai/paper/1704.02463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.02463"}},"official":null}},{"url":"/paper/flow-guided-feature-aggregation-for-video","slug":"flow-guided-feature-aggregation-for-video","title":"Flow-Guided Feature Aggregation for Video Object Detection","date":"2017-03-29","arxiv_id":"1703.10025","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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/flow-guided-feature-aggregation-for-video#ran","syntology_url":"https://syntology.ai/paper/1703.10025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.10025"}},"official":{"repos":["msracver/Flow-Guided-Feature-Aggregation"],"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/adversarial-examples-for-semantic","slug":"adversarial-examples-for-semantic","title":"Adversarial Examples for Semantic Segmentation and Object Detection","date":"2017-03-24","arxiv_id":"1703.08603","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-examples-for-semantic#ran","syntology_url":"https://syntology.ai/paper/1703.08603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.08603"}},"official":null}},{"url":"/paper/mask-r-cnn","slug":"mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","repositories_listed":179,"syntology":{"n":140,"n_ran":101,"n_constructed":3,"n_ran_checked":90,"n_instrument":11,"n_unverified":39,"n_honours":0,"n_violates":0,"n_no_contract":90,"n_pointer_only":32,"phrase":"101 ran (of which 3 constructed an object rather than computing a result; 90 with no instrument failure: 0 honoured, 0 violated, 90 with no contract checked; 11 where Syntology's instrument failed) · 39 unverified","sample_list":"/paper/mask-r-cnn#ran","syntology_url":"https://syntology.ai/paper/1703.06870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06870"}},"official":null}},{"url":"/paper/object-detection-in-videos-with-tubelet","slug":"object-detection-in-videos-with-tubelet","title":"Object Detection in Videos with Tubelet Proposal Networks","date":"2017-02-21","arxiv_id":"1702.06355","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/object-detection-in-videos-with-tubelet#ran","syntology_url":"https://syntology.ai/paper/1702.06355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.06355"}},"official":null}},{"url":"/paper/yolo9000-better-faster-stronger","slug":"yolo9000-better-faster-stronger","title":"YOLO9000: Better, Faster, Stronger","date":"2016-12-25","arxiv_id":"1612.08242","repositories_listed":231,"syntology":{"n":60,"n_ran":44,"n_constructed":0,"n_ran_checked":33,"n_instrument":11,"n_unverified":16,"n_honours":1,"n_violates":4,"n_no_contract":28,"n_pointer_only":23,"phrase":"44 ran (of which 0 constructed an object rather than computing a result; 33 with no instrument failure: 1 honoured, 4 violated, 28 with no contract checked; 11 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/yolo9000-better-faster-stronger#ran","syntology_url":"https://syntology.ai/paper/1612.08242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.08242"}},"official":null}},{"url":"/paper/feature-pyramid-networks-for-object-detection","slug":"feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","date":"2016-12-09","arxiv_id":"1612.03144","repositories_listed":85,"syntology":{"n":51,"n_ran":33,"n_constructed":9,"n_ran_checked":31,"n_instrument":2,"n_unverified":18,"n_honours":1,"n_violates":0,"n_no_contract":30,"n_pointer_only":11,"phrase":"33 ran (of which 9 constructed an object rather than computing a result; 31 with no instrument failure: 1 honoured, 0 violated, 30 with no contract checked; 2 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/feature-pyramid-networks-for-object-detection#ran","syntology_url":"https://syntology.ai/paper/1612.03144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03144"}},"official":null}},{"url":"/paper/learning-video-object-segmentation-from","slug":"learning-video-object-segmentation-from","title":"Learning Video Object Segmentation from Static Images","date":"2016-12-08","arxiv_id":"1612.02646","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-video-object-segmentation-from#ran","syntology_url":"https://syntology.ai/paper/1612.02646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.02646"}},"official":null}},{"url":"/paper/spatially-adaptive-computation-time-for","slug":"spatially-adaptive-computation-time-for","title":"Spatially Adaptive Computation Time for Residual Networks","date":"2016-12-07","arxiv_id":"1612.02297","repositories_listed":1,"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":6,"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/spatially-adaptive-computation-time-for#ran","syntology_url":"https://syntology.ai/paper/1612.02297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.02297"}},"official":{"repos":["mfigurnov/sact"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/squeezedet-unified-small-low-power-fully","slug":"squeezedet-unified-small-low-power-fully","title":"SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving","date":"2016-12-04","arxiv_id":"1612.01051","repositories_listed":12,"syntology":{"n":30,"n_ran":22,"n_constructed":0,"n_ran_checked":22,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":22,"n_pointer_only":0,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/squeezedet-unified-small-low-power-fully#ran","syntology_url":"https://syntology.ai/paper/1612.01051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.01051"}},"official":{"repos":["BichenWuUCB/squeezeDet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/3d-bounding-box-estimation-using-deep","slug":"3d-bounding-box-estimation-using-deep","title":"3D Bounding Box Estimation Using Deep Learning and Geometry","date":"2016-12-01","arxiv_id":"1612.00496","repositories_listed":11,"syntology":{"n":27,"n_ran":19,"n_constructed":0,"n_ran_checked":17,"n_instrument":2,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":16,"n_pointer_only":5,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 0 violated, 16 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/3d-bounding-box-estimation-using-deep#ran","syntology_url":"https://syntology.ai/paper/1612.00496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.00496"}},"official":null}},{"url":"/paper/deep-watershed-transform-for-instance","slug":"deep-watershed-transform-for-instance","title":"Deep Watershed Transform for Instance Segmentation","date":"2016-11-24","arxiv_id":"1611.08303","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/deep-watershed-transform-for-instance#ran","syntology_url":"https://syntology.ai/paper/1611.08303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.08303"}},"official":null}},{"url":"/paper/guesswhat-visual-object-discovery-through","slug":"guesswhat-visual-object-discovery-through","title":"GuessWhat?! Visual object discovery through multi-modal dialogue","date":"2016-11-23","arxiv_id":"1611.08481","repositories_listed":4,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/guesswhat-visual-object-discovery-through#ran","syntology_url":"https://syntology.ai/paper/1611.08481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.08481"}},"official":null}},{"url":"/paper/deeply-supervised-salient-object-detection","slug":"deeply-supervised-salient-object-detection","title":"Deeply supervised salient object detection with short connections","date":"2016-11-15","arxiv_id":"1611.04849","repositories_listed":4,"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/deeply-supervised-salient-object-detection#ran","syntology_url":"https://syntology.ai/paper/1611.04849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.04849"}},"official":null}},{"url":"/paper/learning-a-probabilistic-latent-space-of","slug":"learning-a-probabilistic-latent-space-of","title":"Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling","date":"2016-10-24","arxiv_id":"1610.07584","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":0,"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/learning-a-probabilistic-latent-space-of#ran","syntology_url":"https://syntology.ai/paper/1610.07584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.07584"}},"official":null}},{"url":"/paper/generative-and-discriminative-voxel-modeling","slug":"generative-and-discriminative-voxel-modeling","title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","date":"2016-08-15","arxiv_id":"1608.04236","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-and-discriminative-voxel-modeling#ran","syntology_url":"https://syntology.ai/paper/1608.04236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.04236"}},"official":{"repos":["ajbrock/Generative-and-Discriminative-Voxel-Modeling"],"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/fully-convolutional-siamese-networks-for-1","slug":"fully-convolutional-siamese-networks-for-1","title":"Fully-Convolutional Siamese Networks for Object Tracking","date":"2016-06-30","arxiv_id":"1606.09549","repositories_listed":10,"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/fully-convolutional-siamese-networks-for-1#ran","syntology_url":"https://syntology.ai/paper/1606.09549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.09549"}},"official":{"repos":["bertinetto/siamese-fc"],"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/captioning-images-with-diverse-objects","slug":"captioning-images-with-diverse-objects","title":"Captioning Images with Diverse Objects","date":"2016-06-24","arxiv_id":"1606.07770","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/captioning-images-with-diverse-objects#ran","syntology_url":"https://syntology.ai/paper/1606.07770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.07770"}},"official":null}},{"url":"/paper/end-to-end-instance-segmentation-with","slug":"end-to-end-instance-segmentation-with","title":"End-to-End Instance Segmentation with Recurrent Attention","date":"2016-05-30","arxiv_id":"1605.09410","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/end-to-end-instance-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/1605.09410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09410"}},"official":null}},{"url":"/paper/unsupervised-learning-for-physical","slug":"unsupervised-learning-for-physical","title":"Unsupervised Learning for Physical Interaction through Video Prediction","date":"2016-05-23","arxiv_id":"1605.07157","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-learning-for-physical#ran","syntology_url":"https://syntology.ai/paper/1605.07157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.07157"}},"official":null}},{"url":"/paper/r-fcn-object-detection-via-region-based-fully","slug":"r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","arxiv_id":"1605.06409","repositories_listed":48,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/r-fcn-object-detection-via-region-based-fully#ran","syntology_url":"https://syntology.ai/paper/1605.06409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.06409"}},"official":{"repos":["daijifeng001/r-fcn"],"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/t-cnn-tubelets-with-convolutional-neural","slug":"t-cnn-tubelets-with-convolutional-neural","title":"T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos","date":"2016-04-09","arxiv_id":"1604.02532","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/t-cnn-tubelets-with-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/1604.02532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.02532"}},"official":{"repos":["myfavouritekk/T-CNN"],"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/deep-interactive-object-selection","slug":"deep-interactive-object-selection","title":"Deep Interactive Object Selection","date":"2016-03-13","arxiv_id":"1603.04042","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-interactive-object-selection#ran","syntology_url":"https://syntology.ai/paper/1603.04042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.04042"}},"official":null}},{"url":"/paper/mot16-a-benchmark-for-multi-object-tracking","slug":"mot16-a-benchmark-for-multi-object-tracking","title":"MOT16: A Benchmark for Multi-Object Tracking","date":"2016-03-02","arxiv_id":"1603.00831","repositories_listed":8,"syntology":{"n":38,"n_ran":22,"n_constructed":0,"n_ran_checked":20,"n_instrument":2,"n_unverified":16,"n_honours":8,"n_violates":0,"n_no_contract":12,"n_pointer_only":12,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 8 honoured, 0 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/mot16-a-benchmark-for-multi-object-tracking#ran","syntology_url":"https://syntology.ai/paper/1603.00831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.00831"}},"official":null}},{"url":"/paper/ssd-single-shot-multibox-detector","slug":"ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","arxiv_id":"1512.02325","repositories_listed":221,"syntology":{"n":131,"n_ran":93,"n_constructed":0,"n_ran_checked":79,"n_instrument":14,"n_unverified":38,"n_honours":3,"n_violates":1,"n_no_contract":75,"n_pointer_only":9,"phrase":"93 ran (of which 0 constructed an object rather than computing a result; 79 with no instrument failure: 3 honoured, 1 violated, 75 with no contract checked; 14 where Syntology's instrument failed) · 38 unverified","sample_list":"/paper/ssd-single-shot-multibox-detector#ran","syntology_url":"https://syntology.ai/paper/1512.02325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1512.02325"}},"official":{"repos":["weiliu89/caffe"],"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/natural-language-object-retrieval","slug":"natural-language-object-retrieval","title":"Natural Language Object Retrieval","date":"2015-11-13","arxiv_id":"1511.04164","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/natural-language-object-retrieval#ran","syntology_url":"https://syntology.ai/paper/1511.04164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.04164"}},"official":null}},{"url":"/paper/weakly-supervised-deep-detection-networks","slug":"weakly-supervised-deep-detection-networks","title":"Weakly Supervised Deep Detection Networks","date":"2015-11-09","arxiv_id":"1511.02853","repositories_listed":5,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weakly-supervised-deep-detection-networks#ran","syntology_url":"https://syntology.ai/paper/1511.02853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.02853"}},"official":{"repos":["hbilen/WSDDN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/you-only-look-once-unified-real-time-object","slug":"you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","arxiv_id":"1506.02640","repositories_listed":144,"syntology":{"n":148,"n_ran":89,"n_constructed":27,"n_ran_checked":59,"n_instrument":30,"n_unverified":59,"n_honours":4,"n_violates":4,"n_no_contract":51,"n_pointer_only":98,"phrase":"89 ran (of which 27 constructed an object rather than computing a result; 59 with no instrument failure: 4 honoured, 4 violated, 51 with no contract checked; 30 where Syntology's instrument failed) · 59 unverified","sample_list":"/paper/you-only-look-once-unified-real-time-object#ran","syntology_url":"https://syntology.ai/paper/1506.02640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1506.02640"}},"official":null}},{"url":"/paper/faster-r-cnn-towards-real-time-object","slug":"faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","arxiv_id":"1506.01497","repositories_listed":196,"syntology":{"n":124,"n_ran":79,"n_constructed":9,"n_ran_checked":50,"n_instrument":29,"n_unverified":45,"n_honours":15,"n_violates":5,"n_no_contract":30,"n_pointer_only":44,"phrase":"79 ran (of which 9 constructed an object rather than computing a result; 50 with no instrument failure: 15 honoured, 5 violated, 30 with no contract checked; 29 where Syntology's instrument failed) · 45 unverified","sample_list":"/paper/faster-r-cnn-towards-real-time-object#ran","syntology_url":"https://syntology.ai/paper/1506.01497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1506.01497"}},"official":{"repos":["ShaoqingRen/faster_rcnn","rbgirshick/py-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/learning-to-count-with-deep-object-features","slug":"learning-to-count-with-deep-object-features","title":"Learning to count with deep object features","date":"2015-05-29","arxiv_id":"1505.08082","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-count-with-deep-object-features#ran","syntology_url":"https://syntology.ai/paper/1505.08082","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.08082"}},"official":null}},{"url":"/paper/texture-synthesis-using-convolutional-neural","slug":"texture-synthesis-using-convolutional-neural","title":"Texture Synthesis Using Convolutional Neural Networks","date":"2015-05-27","arxiv_id":"1505.07376","repositories_listed":16,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/texture-synthesis-using-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/1505.07376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.07376"}},"official":{"repos":["leongatys/DeepTextures"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/fast-r-cnn","slug":"fast-r-cnn","title":"Fast R-CNN","date":"2015-04-30","arxiv_id":"1504.08083","repositories_listed":30,"syntology":{"n":8,"n_ran":5,"n_constructed":2,"n_ran_checked":2,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-r-cnn#ran","syntology_url":"https://syntology.ai/paper/1504.08083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.08083"}},"official":{"repos":["rbgirshick/fast-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/multiple-object-recognition-with-visual","slug":"multiple-object-recognition-with-visual","title":"Multiple Object Recognition with Visual Attention","date":"2014-12-24","arxiv_id":"1412.7755","repositories_listed":5,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multiple-object-recognition-with-visual#ran","syntology_url":"https://syntology.ai/paper/1412.7755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1412.7755"}},"official":null}},{"url":"/paper/striving-for-simplicity-the-all-convolutional","slug":"striving-for-simplicity-the-all-convolutional","title":"Striving for Simplicity: The All Convolutional Net","date":"2014-12-21","arxiv_id":"1412.6806","repositories_listed":37,"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/striving-for-simplicity-the-all-convolutional#ran","syntology_url":"https://syntology.ai/paper/1412.6806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1412.6806"}},"official":null}},{"url":"/paper/real-time-grasp-detection-using-convolutional","slug":"real-time-grasp-detection-using-convolutional","title":"Real-Time Grasp Detection Using Convolutional Neural Networks","date":"2014-12-09","arxiv_id":"1412.3128","repositories_listed":3,"syntology":{"n":18,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"10 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; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/real-time-grasp-detection-using-convolutional#ran","syntology_url":"https://syntology.ai/paper/1412.3128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1412.3128"}},"official":null}},{"url":"/paper/hypercolumns-for-object-segmentation-and-fine","slug":"hypercolumns-for-object-segmentation-and-fine","title":"Hypercolumns for Object Segmentation and Fine-grained Localization","date":"2014-11-21","arxiv_id":"1411.5752","repositories_listed":6,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hypercolumns-for-object-segmentation-and-fine#ran","syntology_url":"https://syntology.ai/paper/1411.5752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1411.5752"}},"official":null}},{"url":"/paper/imagenet-large-scale-visual-recognition","slug":"imagenet-large-scale-visual-recognition","title":"ImageNet Large Scale Visual Recognition Challenge","date":"2014-09-01","arxiv_id":"1409.0575","repositories_listed":14,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/imagenet-large-scale-visual-recognition#ran","syntology_url":"https://syntology.ai/paper/1409.0575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1409.0575"}},"official":null}},{"url":"/paper/learning-rich-features-from-rgb-d-images-for","slug":"learning-rich-features-from-rgb-d-images-for","title":"Learning Rich Features from RGB-D Images for Object Detection and Segmentation","date":"2014-07-22","arxiv_id":"1407.5736","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/learning-rich-features-from-rgb-d-images-for#ran","syntology_url":"https://syntology.ai/paper/1407.5736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1407.5736"}},"official":null}},{"url":"/paper/microsoft-coco-common-objects-in-context","slug":"microsoft-coco-common-objects-in-context","title":"Microsoft COCO: Common Objects in Context","date":"2014-05-01","arxiv_id":"1405.0312","repositories_listed":38,"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":0,"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/microsoft-coco-common-objects-in-context#ran","syntology_url":"https://syntology.ai/paper/1405.0312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1405.0312"}},"official":null}}],"record_sha256":"6af8bf7e34a17815c3d3be948967036c4b99d219f7638492dd77d2aff480987d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}