{"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-1/papers/104","list_of":"/task/object-detection-1","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":104,"pages_in_order":106,"rows_per_page":100,"rows":[10301,10400],"of":10514,"counts":{"archive_papers_tagged":10514,"with_a_code_link":4285,"where_syntology_ran_a_sample":1027,"not_listed_spam_title":0,"listed":10514,"listed_where_code_ran":1027,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":898,"every_run_a_failure_of_syntologys_instrument":129,"listed_with_a_run_with_no_instrument_failure":898,"listed_every_run_a_failure_of_syntologys_instrument":129,"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-1","prev":"/task/object-detection-1/papers/103","next":"/task/object-detection-1/papers/105","papers":[{"url":null,"slug":"complexity-adaptive-distance-metric-for","title":"Complexity-Adaptive Distance Metric for Object Proposals Generation","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"encoding-based-saliency-detection-for-videos","title":"Encoding Based Saliency Detection for Videos and Images","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-integration-of-a-convolution","title":"End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enriching-object-detection-with-2d-3d","title":"Enriching Object Detection With 2D-3D Registration and Continuous Viewpoint Estimation","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"expanding-object-detectors-horizon","title":"Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hc-search-for-structured-prediction-in","title":"HC-Search for Structured Prediction in Computer Vision","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"holistic-3d-scene-understanding-from-a-single","title":"Holistic 3D Scene Understanding From a Single Geo-Tagged Image","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-object-proposals-with-multi","title":"Improving Object Proposals With Multi-Thresholding Straddling Expansion","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-sfm-and-detection-cues-for-monocular-3d","title":"Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-coarse-to-fine-sparselets-for","title":"Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-local-and-global-deformations-in","title":"Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-by-labeling-superpixels","title":"Object Detection by Labeling Superpixels","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-proposal-by-multi-branch-hierarchical","title":"Object Proposal by Multi-Branch Hierarchical Segmentation","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-detection-by-multi-context-deep","title":"Saliency Detection by Multi-Context Deep Learning","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-via-bootstrap","title":"Salient Object Detection via Bootstrap Learning","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-object-detection-by-filter","title":"Scalable Object Detection by Filter Compression With Regularized Sparse Coding","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-object-segmentation-via-detection-in","title":"Semantic Object Segmentation via Detection in Weakly Labeled Video","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"small-instance-detection-by-integer","title":"Small Instance Detection by Integer Programming on Object Density Maps","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-convolutional-neural-networks","title":"Sparse Convolutional Neural Networks","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"target-identity-aware-network-flow-for-online","title":"Target Identity-Aware Network Flow for Online Multiple Target Tracking","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"three-viewpoints-toward-exemplar-svm","title":"Three Viewpoints Toward Exemplar SVM","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-3d-object-detection-with-bimodal-deep","title":"Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-visual-alignment-with-similarity","title":"Unsupervised Visual Alignment With Similarity Graphs","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"watch-and-learn-semi-supervised-learning-for","title":"Watch and Learn: Semi-Supervised Learning for Object Detectors From Video","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-object-detection-with-1","title":"Weakly Supervised Object Detection With Convex Clustering","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-via-augmented","title":"Salient Object Detection via Augmented Hypotheses","date":"2015-05-29","arxiv_id":"1505.07930","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-very-deep-convolutional-networks","title":"Accelerating Very Deep Convolutional Networks for Classification and Detection","date":"2015-05-26","arxiv_id":"1505.06798","repositories_listed":0,"syntology":null},{"url":null,"slug":"gazedpm-early-integration-of-gaze-information","title":"GazeDPM: Early Integration of Gaze Information in Deformable Part Models","date":"2015-05-21","arxiv_id":"1505.05753","repositories_listed":0,"syntology":null},{"url":null,"slug":"watch-and-learn-semi-supervised-learning-of","title":"Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos","date":"2015-05-21","arxiv_id":"1505.05769","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-structure-detection-by-context-guided","title":"Salient Structure Detection by Context-Guided Visual Search","date":"2015-05-17","arxiv_id":"1505.04364","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-volumes-for-deep-object-detection","title":"Multi-scale Volumes for Deep Object Detection and Localization","date":"2015-05-14","arxiv_id":"1505.03597","repositories_listed":0,"syntology":null},{"url":null,"slug":"corola-a-sequential-solution-to-moving-object","title":"COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation","date":"2015-05-13","arxiv_id":"1505.03566","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-class-detection-and-classification","title":"Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors","date":"2015-05-03","arxiv_id":"1505.00432","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-interpret-and-describe-abstract","title":"Learning to Interpret and Describe Abstract Scenes","date":"2015-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-sparse-spatial-bins-for-efficient","title":"Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection","date":"2015-04-27","arxiv_id":"1504.07029","repositories_listed":0,"syntology":null},{"url":null,"slug":"mid-level-elements-for-object-detection","title":"Mid-level Elements for Object Detection","date":"2015-04-27","arxiv_id":"1504.07284","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-networks-on-convolutional","title":"Object Detection Networks on Convolutional Feature Maps","date":"2015-04-23","arxiv_id":"1504.06066","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-fisher-vector-a-multimodal","title":"Understanding the Fisher Vector: a multimodal part model","date":"2015-04-18","arxiv_id":"1504.04763","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-object-detection-with-deep","title":"Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction","date":"2015-04-13","arxiv_id":"1504.03293","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multiple-visual-tasks-while","title":"Learning Multiple Visual Tasks while Discovering their Structure","date":"2015-04-13","arxiv_id":"1504.03106","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-coarse-to-fine-model-for-3d-pose-estimation","title":"A Coarse-to-Fine Model for 3D Pose Estimation and Sub-category Recognition","date":"2015-04-10","arxiv_id":"1504.02764","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-eeg-for-object-detection-and","title":"Exploring EEG for Object Detection and Retrieval","date":"2015-04-09","arxiv_id":"1504.02356","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-video-analysis-based-on-a","title":"Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector","date":"2015-03-24","arxiv_id":"1503.06917","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-local-position-estimation-system","title":"Vehicle Local Position Estimation System","date":"2015-03-23","arxiv_id":"1503.06648","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifting-object-detection-datasets-into-3d","title":"Lifting Object Detection Datasets into 3D","date":"2015-03-22","arxiv_id":"1503.06465","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-convolutional-features-for-robust","title":"Boosting Convolutional Features for Robust Object Proposals","date":"2015-03-21","arxiv_id":"1503.06350","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-class-detection-in-the-wild","title":"3D Object Class Detection in the Wild","date":"2015-03-17","arxiv_id":"1503.05038","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-vehicles-by-clustering","title":"Learning to Detect Vehicles by Clustering Appearance Patterns","date":"2015-03-12","arxiv_id":"1503.03771","repositories_listed":0,"syntology":null},{"url":"/paper/fully-connected-deep-structured-networks","slug":"fully-connected-deep-structured-networks","title":"Fully Connected Deep Structured Networks","date":"2015-03-09","arxiv_id":"1503.02351","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-forest-for-efficient-object-detection","title":"Context Forest for efficient object detection with large mixture models","date":"2015-03-03","arxiv_id":"1503.00787","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-calibration-of-ensemble-of-exemplar","title":"Joint calibration of Ensemble of Exemplar SVMs","date":"2015-03-02","arxiv_id":"1503.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-belief-fusion-for-object-detection","title":"Dynamic Belief Fusion for Object Detection","date":"2015-02-26","arxiv_id":"1502.07643","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-for-effective-detection-proposals","title":"What makes for effective detection proposals?","date":"2015-02-17","arxiv_id":"1502.05082","repositories_listed":0,"syntology":null},{"url":null,"slug":"segdeepm-exploiting-segmentation-and-context","title":"segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection","date":"2015-02-15","arxiv_id":"1502.04275","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-expressive-deep-model-for-human-action","title":"An Expressive Deep Model for Human Action Parsing from A Single Image","date":"2015-02-02","arxiv_id":"1502.00501","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminatively-trained-and-or-graph-models","title":"Discriminatively Trained And-Or Graph Models for Object Shape Detection","date":"2015-02-02","arxiv_id":"1502.00341","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-solid-solution-of-real-time-fire","title":"Towards a solid solution of real-time fire and flame detection","date":"2015-02-02","arxiv_id":"1502.00416","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-learning-for-salient-object","title":"Weakly Supervised Learning for Salient Object Detection","date":"2015-01-29","arxiv_id":"1501.07492","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferring-rich-feature-hierarchies-for","title":"Transferring Rich Feature Hierarchies for Robust Visual Tracking","date":"2015-01-19","arxiv_id":"1501.04587","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-distribution-of-salient-objects-in-web","title":"On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection","date":"2015-01-10","arxiv_id":"1501.03383","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-a-benchmark","title":"Salient Object Detection: A Benchmark","date":"2015-01-05","arxiv_id":"1501.02741","repositories_listed":0,"syntology":null},{"url":null,"slug":"detect2rank-combining-object-detectors-using","title":"Detect2Rank : Combining Object Detectors Using Learning to Rank","date":"2014-12-26","arxiv_id":"1412.7957","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-deep-learning-for-car-detection","title":"Joint Deep Learning for Car Detection","date":"2014-12-25","arxiv_id":"1412.7854","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-joint","title":"Convolutional Neural Networks for joint object detection and pose estimation: A comparative study","date":"2014-12-22","arxiv_id":"1412.7190","repositories_listed":0,"syntology":null},{"url":null,"slug":"half-cnn-a-general-framework-for-whole-image","title":"Half-CNN: A General Framework for Whole-Image Regression","date":"2014-12-22","arxiv_id":"1412.6885","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-deep-convolutional-networks-using","title":"Compressing Deep Convolutional Networks using Vector Quantization","date":"2014-12-18","arxiv_id":"1412.6115","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-part-segmentation-using","title":"Semantic Part Segmentation using Compositional Model combining Shape and Appearance","date":"2014-12-18","arxiv_id":"1412.6124","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepid-net-deformable-deep-convolutional","title":"DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection","date":"2014-12-17","arxiv_id":"1412.5661","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-efficient-forward-and-backward","title":"Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification","date":"2014-12-15","arxiv_id":"1412.4526","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-centric-sampling-for-fine-grained","title":"Object-centric Sampling for Fine-grained Image Classification","date":"2014-12-10","arxiv_id":"1412.3161","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-a-salient-object-a-dataset-and-a","title":"What is a salient object? A dataset and a baseline model for salient object detection","date":"2014-12-08","arxiv_id":"1412.5027","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-high-quality-object-detection","title":"Scalable, High-Quality Object Detection","date":"2014-12-03","arxiv_id":"1412.1441","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepedge-a-multi-scale-bifurcated-deep","title":"DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection","date":"2014-12-02","arxiv_id":"1412.1123","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-optimization-for-average-precision","title":"Efficient Optimization for Average Precision SVM","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-weakly-supervised-data-by-the","title":"Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/local-decorrelation-for-improved-pedestrian","slug":"local-decorrelation-for-improved-pedestrian","title":"Local Decorrelation For Improved Pedestrian Detection","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-cascades-for-multiclass","title":"Multi-Resolution Cascades for Multiclass Object Detection","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"untangling-local-and-global-deformations-in","title":"Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection","date":"2014-11-30","arxiv_id":"1412.0296","repositories_listed":0,"syntology":null},{"url":null,"slug":"category-specific-object-reconstruction-from","title":"Category-Specific Object Reconstruction from a Single Image","date":"2014-11-22","arxiv_id":"1411.6069","repositories_listed":0,"syntology":null},{"url":null,"slug":"conceptlearner-discovering-visual-concepts","title":"ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections","date":"2014-11-19","arxiv_id":"1411.5328","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-integration-of-a-convolutional","title":"End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression","date":"2014-11-19","arxiv_id":"1411.5309","repositories_listed":0,"syntology":null},{"url":null,"slug":"fashion-apparel-detection-the-role-of-deep","title":"Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors","date":"2014-11-19","arxiv_id":"1411.5319","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-a-survey","title":"Salient Object Detection: A Survey","date":"2014-11-18","arxiv_id":"1411.5878","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-scene-understanding-with-detailed-3d","title":"Towards Scene Understanding with Detailed 3D Object Representations","date":"2014-11-18","arxiv_id":"1411.5935","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-baby-learning","title":"Computational Baby Learning","date":"2014-11-11","arxiv_id":"1411.2861","repositories_listed":0,"syntology":null},{"url":"/paper/do-convnets-learn-correspondence","slug":"do-convnets-learn-correspondence","title":"Do Convnets Learn Correspondence?","date":"2014-11-04","arxiv_id":"1411.1091","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-chunking-a-list-prediction-framework","title":"Visual Chunking: A List Prediction Framework for Region-Based Object Detection","date":"2014-10-27","arxiv_id":"1410.7376","repositories_listed":0,"syntology":null},{"url":null,"slug":"foreground-background-segmentation-based-on","title":"Foreground-Background Segmentation Based on Codebook and Edge Detector","date":"2014-10-23","arxiv_id":"1410.6472","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-object-detection-a-discriminative","title":"Salient Object Detection: A Discriminative Regional Feature Integration Approach","date":"2014-10-22","arxiv_id":"1410.5926","repositories_listed":0,"syntology":null},{"url":null,"slug":"memristive-threshold-logic-circuit-design-of","title":"Memristive Threshold Logic Circuit Design of Fast Moving Object Detection","date":"2014-10-06","arxiv_id":"1410.1267","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-representation-using","title":"Deep Learning Representation using Autoencoder for 3D Shape Retrieval","date":"2014-09-25","arxiv_id":"1409.7164","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-more-dropouts-in-pool5-feature-maps-for","title":"Do More Dropouts in Pool5 Feature Maps for Better Object Detection","date":"2014-09-24","arxiv_id":"1409.6911","repositories_listed":0,"syntology":null},{"url":null,"slug":"1-hkust-object-detection-in-ilsvrc-2014","title":"1-HKUST: Object Detection in ILSVRC 2014","date":"2014-09-22","arxiv_id":"1409.6155","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-people-in-cubist-art","title":"Detecting People in Cubist Art","date":"2014-09-22","arxiv_id":"1409.6235","repositories_listed":0,"syntology":null},{"url":null,"slug":"pedestrian-detection-with-spatially-pooled","title":"Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning","date":"2014-09-18","arxiv_id":"1409.5209","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepid-net-multi-stage-and-deformable-deep","title":"DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection","date":"2014-09-11","arxiv_id":"1409.3505","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-evolution-of-first-person-vision-methods","title":"The Evolution of First Person Vision Methods: A Survey","date":"2014-09-04","arxiv_id":"1409.1484","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-speech-recognition","title":"Visual Speech Recognition","date":"2014-09-03","arxiv_id":"1409.1411","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-adaptive-structural-svm-for","title":"Hierarchical Adaptive Structural SVM for Domain Adaptation","date":"2014-08-22","arxiv_id":"1408.5400","repositories_listed":0,"syntology":null},{"url":null,"slug":"2d-view-aggregation-for-lymph-node-detection","title":"2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers","date":"2014-08-14","arxiv_id":"1408.3337","repositories_listed":0,"syntology":null}],"record_sha256":"123126663cc74a73810ca8f62c4d2dcbea76821bc9b8d6217a346aa7a5a8c87f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}