{"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/38","list_of":"/task/object","task":"Object","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":38,"pages_in_order":107,"rows_per_page":100,"rows":[3701,3800],"of":10696,"counts":{"archive_papers_tagged":10696,"with_a_code_link":3979,"where_syntology_ran_a_sample":1043,"not_listed_spam_title":0,"listed":10696,"listed_where_code_ran":1043,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":919,"every_run_a_failure_of_syntologys_instrument":124,"listed_with_a_run_with_no_instrument_failure":919,"listed_every_run_a_failure_of_syntologys_instrument":124,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object","prev":"/task/object/papers/37","next":"/task/object/papers/39","papers":[{"url":"/paper/auto-encoding-progressive-generative","slug":"auto-encoding-progressive-generative","title":"Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes","date":"2019-03-08","arxiv_id":"1903.03477","repositories_listed":1,"syntology":null},{"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/leveraging-shape-completion-for-3d-siamese","slug":"leveraging-shape-completion-for-3d-siamese","title":"Leveraging Shape Completion for 3D Siamese Tracking","date":"2019-03-05","arxiv_id":"1903.01784","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/leveraging-shape-completion-for-3d-siamese#ran","syntology_url":"https://syntology.ai/paper/1903.01784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.01784"}},"official":{"repos":["SilvioGiancola/ShapeCompletion3DTracking"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/cad-net-a-context-aware-detection-network-for","slug":"cad-net-a-context-aware-detection-network-for","title":"CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery","date":"2019-03-03","arxiv_id":"1903.00857","repositories_listed":1,"syntology":null},{"url":"/paper/salient-object-detection-on-hyperspectral","slug":"salient-object-detection-on-hyperspectral","title":"Salient object detection on hyperspectral images using features learned from unsupervised segmentation task","date":"2019-02-28","arxiv_id":"1902.10993","repositories_listed":1,"syntology":null},{"url":"/paper/object-driven-text-to-image-synthesis-via","slug":"object-driven-text-to-image-synthesis-via","title":"Object-driven Text-to-Image Synthesis via Adversarial Training","date":"2019-02-27","arxiv_id":"1902.10740","repositories_listed":1,"syntology":null},{"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/min-entropy-latent-model-for-weakly","slug":"min-entropy-latent-model-for-weakly","title":"Min-Entropy Latent Model for Weakly Supervised Object Detection","date":"2019-02-16","arxiv_id":"1902.06057","repositories_listed":1,"syntology":null},{"url":"/paper/orthographicnet-a-deep-learning-approach-for","slug":"orthographicnet-a-deep-learning-approach-for","title":"OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains","date":"2019-02-08","arxiv_id":"1902.03057","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-mixture-models-with-learnable-deep","slug":"spatial-mixture-models-with-learnable-deep","title":"Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping","date":"2019-02-07","arxiv_id":"1902.02502","repositories_listed":1,"syntology":null},{"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/towards-pedestrian-detection-using-retinanet","slug":"towards-pedestrian-detection-using-retinanet","title":"Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge","date":"2019-02-04","arxiv_id":"1902.01031","repositories_listed":1,"syntology":null},{"url":"/paper/online-multi-object-tracking-with-dual","slug":"online-multi-object-tracking-with-dual","title":"Online Multi-Object Tracking with Dual Matching Attention Networks","date":"2019-02-02","arxiv_id":"1902.00749","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-heterogeneous","slug":"representation-learning-for-heterogeneous","title":"Representation Learning for Heterogeneous Information Networks via Embedding Events","date":"2019-01-29","arxiv_id":"1901.10234","repositories_listed":1,"syntology":null},{"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/object-detection-based-on-region","slug":"object-detection-based-on-region","title":"Object Detection based on Region Decomposition and Assembly","date":"2019-01-24","arxiv_id":"1901.08225","repositories_listed":1,"syntology":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/adaptive-fusion-for-rgb-d-salient-object","slug":"adaptive-fusion-for-rgb-d-salient-object","title":"Adaptive Fusion for RGB-D Salient Object Detection","date":"2019-01-05","arxiv_id":"1901.01369","repositories_listed":1,"syntology":null},{"url":"/paper/generating-multiple-objects-at-spatially","slug":"generating-multiple-objects-at-spatially","title":"Generating Multiple Objects at Spatially Distinct Locations","date":"2019-01-03","arxiv_id":"1901.00686","repositories_listed":1,"syntology":null},{"url":"/paper/informative-object-annotations-tell-me","slug":"informative-object-annotations-tell-me","title":"Informative Object Annotations: Tell Me Something I Don't Know","date":"2018-12-26","arxiv_id":"1812.10358","repositories_listed":1,"syntology":null},{"url":"/paper/animating-arbitrary-objects-via-deep-motion","slug":"animating-arbitrary-objects-via-deep-motion","title":"Animating Arbitrary Objects via Deep Motion Transfer","date":"2018-12-20","arxiv_id":"1812.08861","repositories_listed":1,"syntology":null},{"url":"/paper/mid-fusion-octree-based-object-level-multi","slug":"mid-fusion-octree-based-object-level-multi","title":"MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM","date":"2018-12-19","arxiv_id":"1812.07976","repositories_listed":1,"syntology":null},{"url":"/paper/grounded-human-object-interaction-hotspots","slug":"grounded-human-object-interaction-hotspots","title":"Grounded Human-Object Interaction Hotspots from Video","date":"2018-12-11","arxiv_id":"1812.04558","repositories_listed":1,"syntology":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/cross-domain-3d-equivariant-image-embeddings","slug":"cross-domain-3d-equivariant-image-embeddings","title":"Cross-Domain 3D Equivariant Image Embeddings","date":"2018-12-06","arxiv_id":"1812.02716","repositories_listed":1,"syntology":null},{"url":"/paper/visual-object-networks-image-generation-with","slug":"visual-object-networks-image-generation-with","title":"Visual Object Networks: Image Generation with Disentangled 3D Representation","date":"2018-12-06","arxiv_id":"1812.02725","repositories_listed":1,"syntology":null},{"url":"/paper/classifying-collisions-with-spatio-temporal","slug":"classifying-collisions-with-spatio-temporal","title":"Spatio-Temporal Action Graph Networks","date":"2018-12-04","arxiv_id":"1812.01233","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-deep-visual-words-for-fast","slug":"meta-learning-deep-visual-words-for-fast","title":"Meta Learning Deep Visual Words for Fast Video Object Segmentation","date":"2018-12-04","arxiv_id":"1812.01397","repositories_listed":1,"syntology":null},{"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/visual-object-networks-image-generation-with-1","slug":"visual-object-networks-image-generation-with-1","title":"Visual Object Networks: Image Generation with Disentangled 3D Representations","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"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/finegan-unsupervised-hierarchical","slug":"finegan-unsupervised-hierarchical","title":"FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery","date":"2018-11-27","arxiv_id":"1811.11155","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-transformer-network-for-3d-point","slug":"iterative-transformer-network-for-3d-point","title":"Iterative Transformer Network for 3D Point Cloud","date":"2018-11-27","arxiv_id":"1811.11209","repositories_listed":1,"syntology":null},{"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/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/reinforcement-learning-of-active-vision-for","slug":"reinforcement-learning-of-active-vision-for","title":"Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions","date":"2018-11-20","arxiv_id":"1811.08067","repositories_listed":1,"syntology":null},{"url":"/paper/exploit-the-connectivity-multi-object","slug":"exploit-the-connectivity-multi-object","title":"Exploit the Connectivity: Multi-Object Tracking with TrackletNet","date":"2018-11-18","arxiv_id":"1811.07258","repositories_listed":1,"syntology":null},{"url":"/paper/matching-rgb-images-to-cad-models-for-object","slug":"matching-rgb-images-to-cad-models-for-object","title":"Learning Local RGB-to-CAD Correspondences for Object Pose Estimation","date":"2018-11-18","arxiv_id":"1811.07249","repositories_listed":1,"syntology":null},{"url":"/paper/derpn-taking-a-further-step-toward-more","slug":"derpn-taking-a-further-step-toward-more","title":"DeRPN: Taking a further step toward more general object detection","date":"2018-11-16","arxiv_id":"1811.06700","repositories_listed":1,"syntology":null},{"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/ground-plane-polling-for-6dof-pose-estimation","slug":"ground-plane-polling-for-6dof-pose-estimation","title":"Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road","date":"2018-11-16","arxiv_id":"1811.06666","repositories_listed":1,"syntology":null},{"url":"/paper/loans-weakly-supervised-object-detection-with","slug":"loans-weakly-supervised-object-detection-with","title":"LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks","date":"2018-11-14","arxiv_id":"1811.05773","repositories_listed":1,"syntology":null},{"url":"/paper/yolo-lite-a-real-time-object-detection","slug":"yolo-lite-a-real-time-object-detection","title":"YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers","date":"2018-11-14","arxiv_id":"1811.05588","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-of-transfer-learning-in-object","slug":"a-framework-of-transfer-learning-in-object","title":"A Framework of Transfer Learning in Object Detection for Embedded Systems","date":"2018-11-12","arxiv_id":"1811.04863","repositories_listed":1,"syntology":null},{"url":"/paper/parser-extraction-of-triples-in-unstructured","slug":"parser-extraction-of-triples-in-unstructured","title":"Parser Extraction of Triples in Unstructured Text","date":"2018-11-06","arxiv_id":"1811.05768","repositories_listed":1,"syntology":null},{"url":"/paper/the-open-images-dataset-v4-unified-image","slug":"the-open-images-dataset-v4-unified-image","title":"The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale","date":"2018-11-02","arxiv_id":"1811.00982","repositories_listed":1,"syntology":null},{"url":"/paper/class-agnostic-counting","slug":"class-agnostic-counting","title":"Class-Agnostic Counting","date":"2018-11-01","arxiv_id":"1811.00472","repositories_listed":1,"syntology":null},{"url":"/paper/spatialvoc2k-a-multilingual-dataset-of-images","slug":"spatialvoc2k-a-multilingual-dataset-of-images","title":"SpatialVOC2K: A Multilingual Dataset of Images with Annotations and Features for Spatial Relations between Objects","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":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/hybrid-knowledge-routed-modules-for-large","slug":"hybrid-knowledge-routed-modules-for-large","title":"Hybrid Knowledge Routed Modules for Large-scale Object Detection","date":"2018-10-30","arxiv_id":"1810.12681","repositories_listed":1,"syntology":null},{"url":"/paper/deep-affinity-network-for-multiple-object","slug":"deep-affinity-network-for-multiple-object","title":"Deep Affinity Network for Multiple Object Tracking","date":"2018-10-28","arxiv_id":"1810.11780","repositories_listed":1,"syntology":null},{"url":"/paper/scratchdetexploring-to-train-single-shot","slug":"scratchdetexploring-to-train-single-shot","title":"ScratchDet: Training Single-Shot Object Detectors from Scratch","date":"2018-10-19","arxiv_id":"1810.08425","repositories_listed":1,"syntology":null},{"url":"/paper/cure-or-challenging-unreal-and-real","slug":"cure-or-challenging-unreal-and-real","title":"CURE-OR: Challenging Unreal and Real Environments for Object Recognition","date":"2018-10-18","arxiv_id":"1810.08293","repositories_listed":1,"syntology":null},{"url":"/paper/multi-stage-reinforcement-learning-for-object","slug":"multi-stage-reinforcement-learning-for-object","title":"Multi-Stage Reinforcement Learning For Object Detection","date":"2018-10-15","arxiv_id":"1810.10325","repositories_listed":1,"syntology":null},{"url":"/paper/an-evaluation-metric-for-object-detection","slug":"an-evaluation-metric-for-object-detection","title":"An evaluation metric for object detection algorithms in autonomous navigation systems and its application to a real-time alerting system","date":"2018-10-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-online-video-object-segmentation","slug":"unsupervised-online-video-object-segmentation","title":"Unsupervised Online Video Object Segmentation with Motion Property Understanding","date":"2018-10-09","arxiv_id":"1810.03783","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-adversarial-visual-level-domain","slug":"unsupervised-adversarial-visual-level-domain","title":"Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images","date":"2018-10-04","arxiv_id":"1810.02074","repositories_listed":1,"syntology":null},{"url":"/paper/object-detection-from-scratch-with-deep","slug":"object-detection-from-scratch-with-deep","title":"Object Detection from Scratch with Deep Supervision","date":"2018-09-25","arxiv_id":"1809.09294","repositories_listed":1,"syntology":null},{"url":"/paper/deep-part-induction-from-articulated-object","slug":"deep-part-induction-from-articulated-object","title":"Deep Part Induction from Articulated Object Pairs","date":"2018-09-19","arxiv_id":"1809.07417","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/deep-part-induction-from-articulated-object#ran","syntology_url":"https://syntology.ai/paper/1809.07417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.07417"}},"official":{"repos":["ericyi/articulated-part-induction"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/leveraging-contact-forces-for-learning-to","slug":"leveraging-contact-forces-for-learning-to","title":"Leveraging Contact Forces for Learning to Grasp","date":"2018-09-19","arxiv_id":"1809.07004","repositories_listed":1,"syntology":null},{"url":"/paper/focal-loss-in-3d-object-detection","slug":"focal-loss-in-3d-object-detection","title":"Focal Loss in 3D Object Detection","date":"2018-09-17","arxiv_id":"1809.06065","repositories_listed":1,"syntology":null},{"url":"/paper/cadp-a-novel-dataset-for-cctv-traffic-camera","slug":"cadp-a-novel-dataset-for-cctv-traffic-camera","title":"CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis","date":"2018-09-16","arxiv_id":"1809.05782","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-image-classification-via","slug":"multi-label-image-classification-via","title":"Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection","date":"2018-09-16","arxiv_id":"1809.05884","repositories_listed":1,"syntology":null},{"url":"/paper/deep-single-view-3d-object-reconstruction","slug":"deep-single-view-3d-object-reconstruction","title":"Deep Single-View 3D Object Reconstruction with Visual Hull Embedding","date":"2018-09-10","arxiv_id":"1809.03451","repositories_listed":1,"syntology":null},{"url":"/paper/object-hallucination-in-image-captioning","slug":"object-hallucination-in-image-captioning","title":"Object Hallucination in Image Captioning","date":"2018-09-06","arxiv_id":"1809.02156","repositories_listed":1,"syntology":null},{"url":"/paper/contour-knowledge-transfer-for-salient-object","slug":"contour-knowledge-transfer-for-salient-object","title":"Contour Knowledge Transfer for Salient Object Detection","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-ranking-with-soft-consistency-and","slug":"cross-modal-ranking-with-soft-consistency-and","title":"Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/extending-layered-models-to-3d-motion","slug":"extending-layered-models-to-3d-motion","title":"Extending Layered Models to 3D Motion","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/3d-aware-scene-manipulation-via-inverse","slug":"3d-aware-scene-manipulation-via-inverse","title":"3D-Aware Scene Manipulation via Inverse Graphics","date":"2018-08-28","arxiv_id":"1808.09351","repositories_listed":1,"syntology":null},{"url":"/paper/bop-benchmark-for-6d-object-pose-estimation","slug":"bop-benchmark-for-6d-object-pose-estimation","title":"BOP: Benchmark for 6D Object Pose Estimation","date":"2018-08-24","arxiv_id":"1808.08319","repositories_listed":1,"syntology":null},{"url":"/paper/learning-human-object-interactions-by-graph","slug":"learning-human-object-interactions-by-graph","title":"Learning Human-Object Interactions by Graph Parsing Neural Networks","date":"2018-08-23","arxiv_id":"1808.07962","repositories_listed":1,"syntology":null},{"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/distractor-aware-siamese-networks-for-visual","slug":"distractor-aware-siamese-networks-for-visual","title":"Distractor-aware Siamese Networks for Visual Object Tracking","date":"2018-08-18","arxiv_id":"1808.06048","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-visual-recognition-with-salient","slug":"fine-grained-visual-recognition-with-salient","title":"Fine-grained visual recognition with salient feature detection","date":"2018-08-12","arxiv_id":"1808.03935","repositories_listed":1,"syntology":null},{"url":"/paper/the-elephant-in-the-room","slug":"the-elephant-in-the-room","title":"The Elephant in the Room","date":"2018-08-09","arxiv_id":"1808.03305","repositories_listed":1,"syntology":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/universal-perceptual-grouping","slug":"universal-perceptual-grouping","title":"Universal Perceptual Grouping","date":"2018-08-07","arxiv_id":"1808.02312","repositories_listed":1,"syntology":null},{"url":"/paper/parsing-geometry-using-structure-aware-shape","slug":"parsing-geometry-using-structure-aware-shape","title":"Parsing Geometry Using Structure-Aware Shape Templates","date":"2018-08-03","arxiv_id":"1808.01337","repositories_listed":1,"syntology":null},{"url":"/paper/shuffle-then-assemble-learning-object","slug":"shuffle-then-assemble-learning-object","title":"Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features","date":"2018-08-01","arxiv_id":"1808.00171","repositories_listed":1,"syntology":null},{"url":"/paper/tiny-dsod-lightweight-object-detection-for","slug":"tiny-dsod-lightweight-object-detection-for","title":"Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages","date":"2018-07-29","arxiv_id":"1807.11013","repositories_listed":1,"syntology":null},{"url":"/paper/pairwise-body-part-attention-for-recognizing","slug":"pairwise-body-part-attention-for-recognizing","title":"Pairwise Body-Part Attention for Recognizing Human-Object Interactions","date":"2018-07-28","arxiv_id":"1807.10889","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-meaningful-view-selection","slug":"semantically-meaningful-view-selection","title":"Semantically Meaningful View Selection","date":"2018-07-26","arxiv_id":"1807.10303","repositories_listed":1,"syntology":null},{"url":"/paper/self-produced-guidance-for-weakly-supervised","slug":"self-produced-guidance-for-weakly-supervised","title":"Self-produced Guidance for Weakly-supervised Object Localization","date":"2018-07-24","arxiv_id":"1807.08902","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/self-produced-guidance-for-weakly-supervised#ran","syntology_url":"https://syntology.ai/paper/1807.08902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08902"}},"official":{"repos":["xiaomengyc/SPG"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/towards-real-time-accurate-object-detection","slug":"towards-real-time-accurate-object-detection","title":"Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos","date":"2018-07-23","arxiv_id":"1807.08638","repositories_listed":1,"syntology":null},{"url":"/paper/visual-mesh-real-time-object-detection-using","slug":"visual-mesh-real-time-object-detection-using","title":"Visual Mesh: Real-time Object Detection Using Constant Sample Density","date":"2018-07-23","arxiv_id":"1807.08405","repositories_listed":1,"syntology":null},{"url":"/paper/srn-side-output-residual-network-for-object","slug":"srn-side-output-residual-network-for-object","title":"SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond","date":"2018-07-17","arxiv_id":"1807.06621","repositories_listed":1,"syntology":null},{"url":"/paper/functional-object-oriented-network","slug":"functional-object-oriented-network","title":"Functional Object-Oriented Network: Construction & Expansion","date":"2018-07-05","arxiv_id":"1807.02189","repositories_listed":1,"syntology":null},{"url":"/paper/improved-techniques-for-learning-to-dehaze","slug":"improved-techniques-for-learning-to-dehaze","title":"Improved Techniques for Learning to Dehaze and Beyond: A Collective Study","date":"2018-06-30","arxiv_id":"1807.00202","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-extract-fashion-trends-from-social","slug":"how-to-extract-fashion-trends-from-social","title":"How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning","date":"2018-06-28","arxiv_id":"1806.10787","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-annotation-of-semantic-objects","slug":"collaborative-annotation-of-semantic-objects","title":"Collaborative Annotation of Semantic Objects in Images with Multi-granularity Supervisions","date":"2018-06-27","arxiv_id":"1806.10269","repositories_listed":1,"syntology":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/soft-sampling-for-robust-object-detection","slug":"soft-sampling-for-robust-object-detection","title":"Soft Sampling for Robust Object Detection","date":"2018-06-18","arxiv_id":"1806.06986","repositories_listed":1,"syntology":null},{"url":"/paper/object-level-visual-reasoning-in-videos","slug":"object-level-visual-reasoning-in-videos","title":"Object Level Visual Reasoning in Videos","date":"2018-06-16","arxiv_id":"1806.06157","repositories_listed":1,"syntology":null},{"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/doobnet-deep-object-occlusion-boundary","slug":"doobnet-deep-object-occlusion-boundary","title":"DOOBNet: Deep Object Occlusion Boundary Detection from an Image","date":"2018-06-11","arxiv_id":"1806.03772","repositories_listed":1,"syntology":null},{"url":"/paper/dpatch-an-adversarial-patch-attack-on-object","slug":"dpatch-an-adversarial-patch-attack-on-object","title":"DPatch: An Adversarial Patch Attack on Object Detectors","date":"2018-06-05","arxiv_id":"1806.02299","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-convolutional-fusion-for-rgb-d","slug":"recurrent-convolutional-fusion-for-rgb-d","title":"Recurrent Convolutional Fusion for RGB-D Object Recognition","date":"2018-06-05","arxiv_id":"1806.01673","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-of-region","slug":"deep-reinforcement-learning-of-region","title":"Deep Reinforcement Learning of Region Proposal Networks for Object Detection","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/progressive-attention-guided-recurrent","slug":"progressive-attention-guided-recurrent","title":"Progressive Attention Guided Recurrent Network for Salient Object Detection","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"d08c17613dd15bfbec37de41645bd5924095e3381446423e8d7922788eabdf13","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}