{"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-detection/papers/110","list_of":"/task/object-detection","task":"Object Detection","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":110,"pages_in_order":110,"rows_per_page":100,"rows":[10901,10957],"of":10957,"counts":{"archive_papers_tagged":10957,"with_a_code_link":4657,"where_syntology_ran_a_sample":1183,"not_listed_spam_title":0,"listed":10957,"listed_where_code_ran":1183,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":145,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":145,"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-detection","prev":"/task/object-detection/papers/109","next":null,"papers":[{"url":null,"slug":"analyzing-semantic-segmentation-using-hybrid","title":"Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bottom-up-segmentation-for-top-down-detection","title":"Bottom-Up Segmentation for Top-Down Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"category-modeling-from-just-a-single-labeling","title":"Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clam-coupled-localization-and-mapping-with","title":"CLAM: Coupled Localization and Mapping with Efficient Outlier Handling","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-object-reconstruction-with-semantic","title":"Dense Object Reconstruction with Semantic Priors","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-and-aligning-faces-by-image","title":"Detecting and Aligning Faces by Image Retrieval","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-evolution-with-multi-order","title":"Detection Evolution with Multi-order Contextual Co-occurrence","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminatively-trained-and-or-tree-models","title":"Discriminatively Trained And-Or Tree Models for Object Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-detector-adaptation-for-object","title":"Efficient Detector Adaptation for Object Detection in a Video","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-large-scale-structured-learning","title":"Efficient Large-Scale Structured Learning","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-maximum-appearance-search-for-large","title":"Efficient Maximum Appearance Search for Large-Scale Object Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-object-detection-and-segmentation","title":"Efficient Object Detection and Segmentation for Fine-Grained Recognition","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-accurate-detection-of-100000-object","title":"Fast, Accurate Detection of 100,000 Object Classes on a Single Machine","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-multiple-part-based-object-detection","title":"Fast Multiple-Part Based Object Detection Using KD-Ferns","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-object-detection-with-entropy-driven","title":"Fast Object Detection with Entropy-Driven Evaluation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"histograms-of-sparse-codes-for-object","title":"Histograms of Sparse Codes for Object Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"its-not-polite-to-point-describing-people","title":"It's Not Polite to Point: Describing People with Uncertain Attributes","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-detection-tracking-and-mapping-by","title":"Joint Detection, Tracking and Mapping by Semantic Bundle Adjustment","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structured-hough-voting-for-joint","title":"Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-surf-cascade-for-fast-and-accurate","title":"Learning SURF Cascade for Fast and Accurate Object Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-partially-overlapping","title":"Learning to Detect Partially Overlapping Instances","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"part-discovery-from-partial-correspondence","title":"Part Discovery from Partial Correspondence","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-object-co-detection","title":"Robust Object Co-detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seeking-the-strongest-rigid-detector","title":"Seeking the Strongest Rigid Detector","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-paced-learning-for-long-term-tracking","title":"Self-Paced Learning for Long-Term Tracking","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-domain-adaptation-with","title":"Semi-supervised Domain Adaptation with Instance Constraints","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-of-feature","title":"Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-tokens-a-learned-mid-level","title":"Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-deformable-part-models-for","title":"Spatiotemporal Deformable Part Models for Action Detection","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/understanding-indoor-scenes-using-3d","slug":"understanding-indoor-scenes-using-3d","title":"Understanding Indoor Scenes Using 3D Geometric Phrases","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bingham-procrustean-alignment-for-object","title":"Bingham Procrustean Alignment for Object Detection in Clutter","date":"2013-04-27","arxiv_id":"1304.7399","repositories_listed":0,"syntology":null},{"url":null,"slug":"counting-people-from-above-airborne-video","title":"Counting people from above: Airborne video based crowd analysis","date":"2013-04-23","arxiv_id":"1304.6213","repositories_listed":0,"syntology":null},{"url":null,"slug":"shadow-detection-a-survey-and-comparative","title":"Shadow Detection: A Survey and Comparative Evaluation of Recent Methods","date":"2013-04-04","arxiv_id":"1304.1233","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-pruning-for-learning-cascade","title":"Asymmetric Pruning for Learning Cascade Detectors","date":"2013-03-25","arxiv_id":"1303.6066","repositories_listed":0,"syntology":null},{"url":null,"slug":"geodesic-based-salient-object-detection","title":"Geodesic-based Salient Object Detection","date":"2013-02-26","arxiv_id":"1302.6557","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverting-and-visualizing-features-for-object","title":"Inverting and Visualizing Features for Object Detection","date":"2012-12-11","arxiv_id":"1212.2278","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-detection-and-viewpoint-estimation","title":"3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-sensitive-decision-forests-for-object","title":"Context-Sensitive Decision Forests for Object Detection","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-image-descriptors-with-the-boosting","title":"Learning Image Descriptors with the Boosting-Trick","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"timely-object-recognition","title":"Timely Object Recognition","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-recognition-using-embedded-feature","title":"Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"describing-video-contents-in-natural-language","title":"Describing Video Contents in Natural Language","date":"2012-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-margin-multi-label-structured","title":"Maximum Margin Multi-Label Structured Prediction","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-with-grammar-models","title":"Object Detection with Grammar Models","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-deformable-object-detection-using-dual","title":"Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"theta-mrf-capturing-spatial-and-semantic","title":"\\theta-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"moving-object-detection-by-detecting","title":"Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation","date":"2011-09-05","arxiv_id":"1109.0882","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamiltonian-streamline-guided-feature","title":"Hamiltonian Streamline Guided Feature Extraction with Applications to Face Detection","date":"2011-08-17","arxiv_id":"1108.03525","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-cascade-optimization-using-a-product-of","title":"Joint Cascade Optimization Using A Product Of Boosted Classifiers","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-object-detection-and-ranking","title":"Simultaneous Object Detection and Ranking with Weak Supervision","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-holistic-scene-understanding-feedback","title":"Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"face-identification-by-sift-based-complete","title":"Face Identification by SIFT-based Complete Graph Topology","date":"2010-02-02","arxiv_id":"1002.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-multi-class-learning-strategies-in","title":"Evaluating multi-class learning strategies in a generative hierarchical framework for object detection","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"region-based-segmentation-and-object","title":"Region-based Segmentation and Object Detection","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-classification-models-combining","title":"Cascaded Classification Models: Combining Models for Holistic Scene Understanding","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mcboost-multiple-classifier-boosting-for","title":"MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-object-detection-using-a-boosted","title":"Rapid Object Detection using a Boosted Cascade of Simple Features","date":"2003-04-15","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"09c91de0ae76e95214ea2e913c015f3260e72851bcd4f2ba0565ae95d50752c0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}