{"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/106","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":106,"pages_in_order":110,"rows_per_page":100,"rows":[10501,10600],"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/105","next":"/task/object-detection/papers/107","papers":[{"url":null,"slug":"can-you-tell-where-in-india-i-am-from","title":"Can you tell where in India I am from? Comparing humans and computers on fine-grained race face classification","date":"2017-03-22","arxiv_id":"1703.07595","repositories_listed":0,"syntology":null},{"url":null,"slug":"iod-cnn-integrating-object-detection-networks","title":"IOD-CNN: Integrating Object Detection Networks for Event Recognition","date":"2017-03-21","arxiv_id":"1703.07431","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-based-real-time-aerial-object","title":"Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System","date":"2017-03-19","arxiv_id":"1703.06527","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-hog-descriptor-using-lookup-table-and","title":"A Fast HOG Descriptor Using Lookup Table and Integral Image","date":"2017-03-18","arxiv_id":"1703.06256","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-deep-convolutional-neural-networks","title":"Evaluating Deep Convolutional Neural Networks for Material Classification","date":"2017-03-12","arxiv_id":"1703.04101","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-3d-object-detection-and-pose-estimation","title":"A 3D Object Detection and Pose Estimation Pipeline Using RGB-D Images","date":"2017-03-11","arxiv_id":"1703.03940","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-an-android-application-for","title":"Development of An Android Application for Object Detection Based on Color, Shape, or Local Features","date":"2017-03-10","arxiv_id":"1703.03848","repositories_listed":0,"syntology":null},{"url":"/paper/bridging-saliency-detection-to-weakly","slug":"bridging-saliency-detection-to-weakly","title":"Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning","date":"2017-03-03","arxiv_id":"1703.01290","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robot-activities-from-first-person","title":"Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression","date":"2017-03-03","arxiv_id":"1703.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-object-detection-with-region","title":"Improving Object Detection with Region Similarity Learning","date":"2017-03-01","arxiv_id":"1703.00234","repositories_listed":0,"syntology":null},{"url":null,"slug":"rgb-d-salient-object-detection-based-on","title":"RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning","date":"2017-03-01","arxiv_id":"1703.00122","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-and-semi-supervised-object-detection","title":"Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm","date":"2017-02-28","arxiv_id":"1702.08740","repositories_listed":0,"syntology":null},{"url":"/paper/a-dataset-for-developing-and-benchmarking","slug":"a-dataset-for-developing-and-benchmarking","title":"A Dataset for Developing and Benchmarking Active Vision","date":"2017-02-27","arxiv_id":"1702.08272","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-privacy-preserving-viola-jones-type","title":"Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation","date":"2017-02-27","arxiv_id":"1702.08318","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-lesion-classification-using-hybrid-deep","title":"Skin Lesion Classification Using Hybrid Deep Neural Networks","date":"2017-02-27","arxiv_id":"1702.08434","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-what-is-not-there-learning-context-to","title":"Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing","date":"2017-02-26","arxiv_id":"1702.07971","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-training-data-for-object","title":"Synthesizing Training Data for Object Detection in Indoor Scenes","date":"2017-02-25","arxiv_id":"1702.07836","repositories_listed":0,"syntology":null},{"url":null,"slug":"viewpoint-adaptation-for-rigid-object","title":"Viewpoint Adaptation for Rigid Object Detection","date":"2017-02-24","arxiv_id":"1702.07451","repositories_listed":0,"syntology":null},{"url":null,"slug":"vip-cnn-visual-phrase-guided-convolutional","title":"ViP-CNN: Visual Phrase Guided Convolutional Neural Network","date":"2017-02-23","arxiv_id":"1702.07191","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-deep-learning-in-medical-image","title":"A Survey on Deep Learning in Medical Image Analysis","date":"2017-02-19","arxiv_id":"1702.05747","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-deep-reinforcement-learning-for-1","title":"Collaborative Deep Reinforcement Learning for Joint Object Search","date":"2017-02-18","arxiv_id":"1702.05573","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-visual-applications-a","title":"Domain Adaptation for Visual Applications: A Comprehensive Survey","date":"2017-02-17","arxiv_id":"1702.05374","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-discovery-at-pinterest","title":"Visual Discovery at Pinterest","date":"2017-02-15","arxiv_id":"1702.04680","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentional-network-for-visual-object","title":"Attentional Network for Visual Object Detection","date":"2017-02-06","arxiv_id":"1702.01478","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-experimental-study-of-deep-convolutional","title":"An Experimental Study of Deep Convolutional Features For Iris Recognition","date":"2017-02-04","arxiv_id":"1702.01334","repositories_listed":0,"syntology":null},{"url":null,"slug":"wide-residual-inception-networks-for-real","title":"Wide-Residual-Inception Networks for Real-time Object Detection","date":"2017-02-04","arxiv_id":"1702.01243","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-salient-object-detection-via-fully","title":"Video Salient Object Detection via Fully Convolutional Networks","date":"2017-02-02","arxiv_id":"1702.00871","repositories_listed":0,"syntology":null},{"url":"/paper/youtube-boundingboxes-a-large-high-precision","slug":"youtube-boundingboxes-a-large-high-precision","title":"YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video","date":"2017-02-02","arxiv_id":"1702.00824","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-wise-ear-detection-with-convolutional","title":"Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks","date":"2017-02-01","arxiv_id":"1702.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-for-robot-perception","title":"Incremental Learning for Robot Perception through HRI","date":"2017-01-17","arxiv_id":"1701.04693","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandwidth-limited-object-recognition-in-high","title":"Bandwidth limited object recognition in high resolution imagery","date":"2017-01-16","arxiv_id":"1701.04210","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-salient-object-detection-for","title":"Hierarchical Salient Object Detection for Assisted Grasping","date":"2017-01-16","arxiv_id":"1701.04284","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-pursuit-a-bayesian-framework-for","title":"Information Pursuit: A Bayesian Framework for Sequential Scene Parsing","date":"2017-01-09","arxiv_id":"1701.02343","repositories_listed":0,"syntology":null},{"url":"/paper/to-boost-or-not-to-boost-on-the-limits-of","slug":"to-boost-or-not-to-boost-on-the-limits-of","title":"To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection","date":"2017-01-06","arxiv_id":"1701.01692","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-driven-object-detection-with-top-down","title":"Action-Driven Object Detection with Top-Down Visual Attentions","date":"2016-12-20","arxiv_id":"1612.06704","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-and-training-of-low-bit-width","title":"Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection","date":"2016-12-19","arxiv_id":"1612.06052","repositories_listed":0,"syntology":null},{"url":null,"slug":"salient-region-detection-with-convex-hull","title":"Salient Object Detection with Convex Hull Overlap","date":"2016-12-10","arxiv_id":"1612.03284","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-hand-detection-and-rotation-estimation","title":"Joint Hand Detection and Rotation Estimation by Using CNN","date":"2016-12-08","arxiv_id":"1612.02742","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-pair-feature-based-object-detection-for","title":"Point Pair Feature based Object Detection for Random Bin Picking","date":"2016-12-05","arxiv_id":"1612.01288","repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-segment-object-candidates-via","slug":"learning-to-segment-object-candidates-via","title":"Learning to Segment Object Candidates via Recursive Neural Networks","date":"2016-12-04","arxiv_id":"1612.01057","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-cancer-detection-and-tracking-using-data","title":"Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning","date":"2016-12-04","arxiv_id":"1612.01074","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-via-aspect-ratio-and-context","title":"Object Detection via Aspect Ratio and Context Aware Region-based Convolutional Networks","date":"2016-12-02","arxiv_id":"1612.00534","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-in-an-uncertain-world-representing","title":"Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses","date":"2016-12-01","arxiv_id":"1612.00197","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsetnet-predicting-sets-with-deep-neural","title":"DeepSetNet: Predicting Sets with Deep Neural Networks","date":"2016-11-28","arxiv_id":"1611.08998","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-holistic-3d-scene-abstractions-for","title":"Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval","date":"2016-11-28","arxiv_id":"1611.09392","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-free-instance-segmentation","title":"Object Detection Free Instance Segmentation With Labeling Transformations","date":"2016-11-28","arxiv_id":"1611.08991","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-on-data-representation-learning","title":"An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning","date":"2016-11-25","arxiv_id":"1611.08331","repositories_listed":0,"syntology":null},{"url":"/paper/weakly-supervised-cascaded-convolutional","slug":"weakly-supervised-cascaded-convolutional","title":"Weakly Supervised Cascaded Convolutional Networks","date":"2016-11-24","arxiv_id":"1611.08258","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-using-image-processing","title":"Object Detection using Image Processing","date":"2016-11-23","arxiv_id":"1611.07791","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-with-version-spaces-for","title":"Active learning with version spaces for object detection","date":"2016-11-22","arxiv_id":"1611.07285","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-web-images-for-dataset","title":"Exploiting Web Images for Dataset Construction: A Domain Robust Approach","date":"2016-11-22","arxiv_id":"1611.07156","repositories_listed":0,"syntology":null},{"url":null,"slug":"resfeats-residual-network-based-features-for","title":"ResFeats: Residual Network Based Features for Image Classification","date":"2016-11-21","arxiv_id":"1611.06656","repositories_listed":0,"syntology":null},{"url":null,"slug":"nazr-cnn-fine-grained-classification-of-uav","title":"Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment","date":"2016-11-20","arxiv_id":"1611.06474","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-saliency-detection-using","title":"Multi-Scale Saliency Detection using Dictionary Learning","date":"2016-11-19","arxiv_id":"1611.06307","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepvo-a-deep-learning-approach-for-monocular","title":"DeepVO: A Deep Learning approach for Monocular Visual Odometry","date":"2016-11-18","arxiv_id":"1611.06069","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-training-of-deep-neural-networks","title":"Improving training of deep neural networks via Singular Value Bounding","date":"2016-11-18","arxiv_id":"1611.06013","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-and-localize-many-objects","title":"Learning to detect and localize many objects from few examples","date":"2016-11-17","arxiv_id":"1611.05664","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-processing-from-electro-optical-sensors","title":"Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey","date":"2016-11-17","arxiv_id":"1611.05842","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-learning-of-mid-level","title":"Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization","date":"2016-11-17","arxiv_id":"1611.05603","repositories_listed":0,"syntology":null},{"url":null,"slug":"backtracking-spatial-pyramid-pooling-spp","title":"Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection","date":"2016-11-16","arxiv_id":"1611.05345","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-scene-specific-object-detectors","title":"Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision","date":"2016-11-12","arxiv_id":"1611.03968","repositories_listed":0,"syntology":null},{"url":null,"slug":"node-adapt-path-adapt-and-tree-adaptmodel","title":"Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest","date":"2016-11-09","arxiv_id":"1611.02886","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-benchmark-dataset-and-saliency-guided","title":"A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection","date":"2016-11-01","arxiv_id":"1611.00135","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-haar-filter-based-deep-networks","title":"Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene","date":"2016-10-30","arxiv_id":"1610.09609","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdbrief-a-fast-online-adaptable-distorted","title":"mdBrief - A Fast Online Adaptable, Distorted Binary Descriptor for Real-Time Applications Using Calibrated Wide-Angle Or Fisheye Cameras","date":"2016-10-25","arxiv_id":"1610.07804","repositories_listed":0,"syntology":null},{"url":null,"slug":"template-matching-advances-and-applications","title":"Template Matching Advances and Applications in Image Analysis","date":"2016-10-23","arxiv_id":"1610.07231","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-object-detection-via-fusion-with","title":"Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification","date":"2016-10-21","arxiv_id":"1610.06907","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-metric-learning-for-multi-instance","title":"Multi-view metric learning for multi-instance image classification","date":"2016-10-21","arxiv_id":"1610.06671","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-3d-2d-interactive-tool-for-scene","title":"A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation","date":"2016-10-19","arxiv_id":"1610.05883","repositories_listed":0,"syntology":null},{"url":"/paper/deep-fruit-detection-in-orchards","slug":"deep-fruit-detection-in-orchards","title":"Deep Fruit Detection in Orchards","date":"2016-10-12","arxiv_id":"1610.03677","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-depth-from-single-monocular-images","title":"Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation","date":"2016-10-06","arxiv_id":"1610.01706","repositories_listed":0,"syntology":null},{"url":null,"slug":"pano2cad-room-layout-from-a-single-panorama","title":"Pano2CAD: Room Layout From A Single Panorama Image","date":"2016-09-29","arxiv_id":"1609.09270","repositories_listed":0,"syntology":null},{"url":"/paper/multiview-rgb-d-dataset-for-object-instance","slug":"multiview-rgb-d-dataset-for-object-instance","title":"Multiview RGB-D Dataset for Object Instance Detection","date":"2016-09-26","arxiv_id":"1609.07826","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-usability-of-deep-networks-for-object","title":"On the usability of deep networks for object-based image analysis","date":"2016-09-22","arxiv_id":"1609.06845","repositories_listed":0,"syntology":null},{"url":null,"slug":"glasses-detection-using-convolutional-neural","title":"Glasses Detection Using Convolutional Neural Networks","date":"2016-09-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/vote3deep-fast-object-detection-in-3d-point","slug":"vote3deep-fast-object-detection-in-3d-point","title":"Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks","date":"2016-09-21","arxiv_id":"1609.06666","repositories_listed":0,"syntology":null},{"url":null,"slug":"gadaboost-accelerating-adaboost-feature","title":"GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms","date":"2016-09-20","arxiv_id":"1609.06260","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-saliency-estimation","title":"Probabilistic Saliency Estimation","date":"2016-09-13","arxiv_id":"1609.03868","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-semantic-part-based-models-from","title":"Learning Semantic Part-Based Models from Google Images","date":"2016-09-11","arxiv_id":"1609.03140","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-context-selection-in-object","title":"The Role of Context Selection in Object Detection","date":"2016-09-09","arxiv_id":"1609.02948","repositories_listed":0,"syntology":null},{"url":null,"slug":"bottom-up-instance-segmentation-using-deep","title":"Bottom-up Instance Segmentation using Deep Higher-Order CRFs","date":"2016-09-08","arxiv_id":"1609.02583","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-preserving-and-multi-scale-contextual","title":"Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection","date":"2016-08-29","arxiv_id":"1608.08029","repositories_listed":0,"syntology":null},{"url":"/paper/vehicle-detection-from-3d-lidar-using-fully","slug":"vehicle-detection-from-3d-lidar-using-fully","title":"Vehicle Detection from 3D Lidar Using Fully Convolutional Network","date":"2016-08-29","arxiv_id":"1608.07916","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-proposals-using-stereo-imagery-for","title":"3D Object Proposals using Stereo Imagery for Accurate Object Class Detection","date":"2016-08-27","arxiv_id":"1608.07711","repositories_listed":0,"syntology":null},{"url":"/paper/a-4d-light-field-dataset-and-cnn","slug":"a-4d-light-field-dataset-and-cnn","title":"A 4D Light-Field Dataset and CNN Architectures for Material Recognition","date":"2016-08-24","arxiv_id":"1608.06985","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-object-detection-with-group","title":"Multi-stage Object Detection with Group Recursive Learning","date":"2016-08-18","arxiv_id":"1608.05159","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-tracking-and-motion","title":"Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation","date":"2016-08-10","arxiv_id":"1608.03066","repositories_listed":0,"syntology":null},{"url":null,"slug":"onionnet-sharing-features-in-cascaded-deep","title":"OnionNet: Sharing Features in Cascaded Deep Classifiers","date":"2016-08-09","arxiv_id":"1608.02728","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-change-retrieval-for-moving","title":"Compressive Change Retrieval for Moving Object Detection","date":"2016-08-06","arxiv_id":"1608.02051","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-deep-convolutional-networks-for-large","title":"Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification","date":"2016-08-05","arxiv_id":"1608.01866","repositories_listed":0,"syntology":null},{"url":null,"slug":"unitbox-an-advanced-object-detection-network","title":"UnitBox: An Advanced Object Detection Network","date":"2016-08-04","arxiv_id":"1608.01471","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-in-video-based-object-detection-in","title":"Challenges in video based object detection in maritime scenario using computer vision","date":"2016-08-03","arxiv_id":"1608.01079","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-lexical-and-spatial-knowledge-to","title":"Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-captions-in-the-wild-to-improve","title":"Leveraging Captions in the Wild to Improve Object Detection","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-586mw-real-time-programmable-object","title":"A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps","date":"2016-07-27","arxiv_id":"1607.08635","repositories_listed":0,"syntology":null},{"url":"/paper/salient-object-subitizing","slug":"salient-object-subitizing","title":"Salient Object Subitizing","date":"2016-07-26","arxiv_id":"1607.07525","repositories_listed":0,"syntology":null},{"url":"/paper/is-faster-r-cnn-doing-well-for-pedestrian","slug":"is-faster-r-cnn-doing-well-for-pedestrian","title":"Is Faster R-CNN Doing Well for Pedestrian Detection?","date":"2016-07-24","arxiv_id":"1607.07032","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-cut-formulation-for-joint","title":"A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects","date":"2016-07-21","arxiv_id":"1607.06317","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-of-local-rgb-d-patches-for-3d","title":"Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation","date":"2016-07-20","arxiv_id":"1607.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"hashmod-a-hashing-method-for-scalable-3d","title":"Hashmod: A Hashing Method for Scalable 3D Object Detection","date":"2016-07-20","arxiv_id":"1607.06062","repositories_listed":0,"syntology":null}],"record_sha256":"f0390efb7558345a234c829de02db6467606c4206cd228cb9f765029ac10e2cd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}