{"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/102","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":102,"pages_in_order":106,"rows_per_page":100,"rows":[10101,10200],"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/101","next":"/task/object-detection-1/papers/103","papers":[{"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":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":"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},{"url":null,"slug":"recycle-deep-features-for-better-object","title":"Recycle deep features for better object detection","date":"2016-07-18","arxiv_id":"1607.05066","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-object-detection-using","title":"Weakly supervised object detection using pseudo-strong labels","date":"2016-07-16","arxiv_id":"1607.04731","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-matters-refining-object-detection-in","title":"Context Matters: Refining Object Detection in Video with Recurrent Neural Networks","date":"2016-07-15","arxiv_id":"1607.04648","repositories_listed":0,"syntology":null},{"url":"/paper/rgbd-salient-object-detection-via-deep-fusion","slug":"rgbd-salient-object-detection-via-deep-fusion","title":"RGBD Salient Object Detection via Deep Fusion","date":"2016-07-12","arxiv_id":"1607.03333","repositories_listed":0,"syntology":null},{"url":null,"slug":"intra-layer-nonuniform-quantization-for-deep","title":"Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network","date":"2016-07-10","arxiv_id":"1607.02720","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-concealed-cars-in-complex-cargo","title":"Detection of concealed cars in complex cargo X-ray imagery using Deep Learning","date":"2016-06-26","arxiv_id":"1606.08078","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-lidar-based-depth-mapping","title":"High-resolution LIDAR-based Depth Mapping using Bilateral Filter","date":"2016-06-17","arxiv_id":"1606.05614","repositories_listed":0,"syntology":null},{"url":null,"slug":"znni-maximizing-the-inference-throughput-of","title":"ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs","date":"2016-06-17","arxiv_id":"1606.05688","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-inertial-semantic-scene-representation","title":"Visual-Inertial-Semantic Scene Representation for 3-D Object Detection","date":"2016-06-13","arxiv_id":"1606.03968","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-networks-for-high-accuracy-road","title":"Shallow Networks for High-Accuracy Road Object-Detection","date":"2016-06-05","arxiv_id":"1606.01561","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-continuous-occlusion-model-for-road-scene","title":"A Continuous Occlusion Model for Road Scene Understanding","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"backtracking-scspm-image-classifier-for","title":"Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dhsnet-deep-hierarchical-saliency-network-for","slug":"dhsnet-deep-hierarchical-saliency-network-for","title":"DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dictionary-pair-classifier-driven","title":"Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-point-process-inference-for-large","title":"Efficient Point Process Inference for Large-Scale Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploit-all-the-layers-fast-and-accurate-cnn","title":"Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"factors-in-finetuning-deep-model-for-object-1","title":"Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-hard-sample-mining-with-monte","title":"Large Scale Hard Sample Mining With Monte Carlo Tree Search","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-semi-supervised-object-detection","title":"Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-co-generate-object-proposals-with","title":"Learning to Co-Generate Object Proposals With a Deep Structured Network","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-side-information-through","title":"Learning With Side Information Through Modality Hallucination","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/local-background-enclosure-for-rgb-d-salient","slug":"local-background-enclosure-for-rgb-d-salient","title":"Local Background Enclosure for RGB-D Salient Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/monocular-3d-object-detection-for-autonomous","slug":"monocular-3d-object-detection-for-autonomous","title":"Monocular 3D Object Detection for Autonomous Driving","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-from-structure-mfs-searching-for-3d","title":"Motion From Structure (MfS): Searching for 3D Objects in Cluttered Point Trajectories","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"progressively-parsing-interactional-objects","title":"Progressively Parsing Interactional Objects for Fine Grained Action Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-for-visual-object","title":"Reinforcement Learning for Visual Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rifd-cnn-rotation-invariant-and-fisher","title":"RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-from-motion-with-objects","title":"Structure From Motion With Objects","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/three-dimensional-object-detection-and-layout","slug":"three-dimensional-object-detection-and-layout","title":"Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-discriminative","title":"Unsupervised Learning of Discriminative Attributes and Visual Representations","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-wrong-with-that-object-identifying","title":"What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stacking-with-auxiliary-features","title":"Stacking With Auxiliary Features","date":"2016-05-27","arxiv_id":"1605.08764","repositories_listed":0,"syntology":null},{"url":null,"slug":"quickest-moving-object-detection","title":"Quickest Moving Object Detection","date":"2016-05-24","arxiv_id":"1605.07369","repositories_listed":0,"syntology":null},{"url":"/paper/virtual-worlds-as-proxy-for-multi-object","slug":"virtual-worlds-as-proxy-for-multi-object","title":"Virtual Worlds as Proxy for Multi-Object Tracking Analysis","date":"2016-05-20","arxiv_id":"1605.06457","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-ranking-object-proposals-for-object","title":"Re-ranking Object Proposals for Object Detection in Automatic Driving","date":"2016-05-19","arxiv_id":"1605.05904","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fly-network-pruning-for-object","title":"On-the-fly Network Pruning for Object Detection","date":"2016-05-11","arxiv_id":"1605.03477","repositories_listed":0,"syntology":null},{"url":null,"slug":"unconstrained-stillvideo-based-face","title":"Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks","date":"2016-05-09","arxiv_id":"1605.02686","repositories_listed":0,"syntology":null}],"record_sha256":"37e06798082f469fa3193a9c424412900c24832d0e92fe02fe6354e5105ffb50","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}