{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object/papers/87","list_of":"/task/object","task":"Object","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":87,"pages_in_order":107,"rows_per_page":100,"rows":[8601,8700],"of":10696,"counts":{"archive_papers_tagged":10696,"with_a_code_link":3979,"where_syntology_ran_a_sample":1043,"not_listed_spam_title":0,"listed":10696,"listed_where_code_ran":1043,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":919,"every_run_a_failure_of_syntologys_instrument":124,"listed_with_a_run_with_no_instrument_failure":919,"listed_every_run_a_failure_of_syntologys_instrument":124,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object","prev":"/task/object/papers/86","next":"/task/object/papers/88","papers":[{"url":"/paper/perspectivenet-3d-object-detection-from-a-1","slug":"perspectivenet-3d-object-detection-from-a-1","title":"PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points","date":"2019-12-16","arxiv_id":"1912.07744","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-with-contextual-cueing-for","title":"Towards Contextual Learning in Few-shot Object Classification","date":"2019-12-13","arxiv_id":"1912.06679","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-object-detection-using-context-and","title":"Small Object Detection using Context and Attention","date":"2019-12-13","arxiv_id":"1912.06319","repositories_listed":0,"syntology":null},{"url":null,"slug":"l3dor-lifelong-3d-object-recognition","title":"L3DOC: Lifelong 3D Object Classification","date":"2019-12-12","arxiv_id":"1912.06135","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-recognition-with-human-in-the-loop","title":"Object Recognition with Human in the Loop Intelligent Frameworks","date":"2019-12-11","arxiv_id":"1912.05575","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-segmenting-and-tracking-object","title":"Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation","date":"2019-12-10","arxiv_id":"1912.04573","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-layout-transfer-network-for","title":"Learning a Layout Transfer Network for Context Aware Object Detection","date":"2019-12-09","arxiv_id":"1912.03865","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-object-motion-and-depth","title":"Self-supervised Object Motion and Depth Estimation from Video","date":"2019-12-09","arxiv_id":"1912.04250","repositories_listed":0,"syntology":null},{"url":null,"slug":"300-ghz-radar-object-recognition-based-on","title":"300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning","date":"2019-12-06","arxiv_id":"1912.03157","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-capsule-autoencoders-for-3d-point","title":"Geometric Capsule Autoencoders for 3D Point Clouds","date":"2019-12-06","arxiv_id":"1912.03310","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-generation-of-human-object","title":"Generating Videos of Zero-Shot Compositions of Actions and Objects","date":"2019-12-05","arxiv_id":"1912.02401","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-projective-image-rectification-for","title":"Fast Projective Image Rectification for Planar Objects with Manhattan Structure","date":"2019-12-04","arxiv_id":"1912.01892","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-object-tracking-and","title":"Learning Multi-Object Tracking and Segmentation from Automatic Annotations","date":"2019-12-04","arxiv_id":"1912.02096","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-with-convolutional-neural","title":"Object Detection with Convolutional Neural Networks","date":"2019-12-04","arxiv_id":"1912.01844","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-video-object-segmentation-via","title":"Automatic Video Object Segmentation via Motion-Appearance-Stream Fusion and Instance-aware Segmentation","date":"2019-12-03","arxiv_id":"1912.01373","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-gan-do-better-gan-based-detection-of","title":"It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality","date":"2019-12-03","arxiv_id":"1912.01707","repositories_listed":0,"syntology":null},{"url":null,"slug":"ienet-interacting-embranchment-one-stage","title":"IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection","date":"2019-12-02","arxiv_id":"1912.00969","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-model-drift-for-robust-object","title":"Improving Model Drift for Robust Object Tracking","date":"2019-12-02","arxiv_id":"1912.00826","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-neural-architecture-transformation-1","title":"Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/objectnet-a-large-scale-bias-controlled","slug":"objectnet-a-large-scale-bias-controlled","title":"ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-graph-network-for-3d-object","title":"Relation Graph Network for 3D Object Detection in Point Clouds","date":"2019-11-30","arxiv_id":"1912.00202","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-semantic-correspondence-exploiting","title":"Learning Semantic Correspondence Exploiting an Object-level Prior","date":"2019-11-29","arxiv_id":"1911.12914","repositories_listed":0,"syntology":null},{"url":"/paper/continuous-adaptation-for-interactive-object","slug":"continuous-adaptation-for-interactive-object","title":"Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections","date":"2019-11-28","arxiv_id":"1911.12709","repositories_listed":0,"syntology":null},{"url":"/paper/pointrgcn-graph-convolution-networks-for-3d","slug":"pointrgcn-graph-convolution-networks-for-3d","title":"PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement","date":"2019-11-27","arxiv_id":"1911.12236","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-bi-fusion-feature-pyramid-network","title":"Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection","date":"2019-11-27","arxiv_id":"1911.12051","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-portion-tracking-in-4d","title":"Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps","date":"2019-11-26","arxiv_id":"1911.11808","repositories_listed":0,"syntology":null},{"url":"/paper/wsod-with-psnet-and-box-regression","slug":"wsod-with-psnet-and-box-regression","title":"WSOD with PSNet and Box Regression","date":"2019-11-26","arxiv_id":"1911.11512","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-object-tracking-via-meta-learning","title":"Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning","date":"2019-11-25","arxiv_id":"1911.11170","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multivariate-mixture-of-gaussians-for","title":"Deep Mixture Density Network for Probabilistic Object Detection","date":"2019-11-24","arxiv_id":"1911.10614","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-meta-learning-model-for-few","title":"Differentiable Meta-learning Model for Few-shot Semantic Segmentation","date":"2019-11-23","arxiv_id":"1911.10371","repositories_listed":0,"syntology":null},{"url":null,"slug":"sm-nas-structural-to-modular-neural","title":"SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection","date":"2019-11-22","arxiv_id":"1911.09929","repositories_listed":0,"syntology":null},{"url":null,"slug":"refinedmpl-refined-monocular-pseudolidar-for","title":"RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving","date":"2019-11-21","arxiv_id":"1911.09712","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-object-segmentation-with","title":"Unsupervised Object Segmentation with Explicit Localization Module","date":"2019-11-21","arxiv_id":"1911.09228","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-tactile-fusion-object-clustering","title":"Visual Tactile Fusion Object Clustering","date":"2019-11-21","arxiv_id":"1911.09430","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-invariant-adaptive-object-detection","title":"Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement","date":"2019-11-20","arxiv_id":"1911.08712","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-guided-instance-segmentation-for","title":"Object-Guided Instance Segmentation for Biological Images","date":"2019-11-20","arxiv_id":"1911.09199","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-3d-objects-from","title":"Self-supervised Learning of 3D Objects from Natural Images","date":"2019-11-20","arxiv_id":"1911.08850","repositories_listed":0,"syntology":null},{"url":null,"slug":"tell-me-what-theyre-holding-weakly-supervised","title":"Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction","date":"2019-11-19","arxiv_id":"1911.08141","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-image-co-segmentation-a-survey","title":"Automatic Image Co-Segmentation: A Survey","date":"2019-11-18","arxiv_id":"1911.07685","repositories_listed":0,"syntology":null},{"url":null,"slug":"dont-even-look-once-synthesizing-features-for","title":"Dont Even Look Once: Synthesizing Features for Zero-Shot Detection","date":"2019-11-18","arxiv_id":"1911.07933","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-reinforcement-learning-of","title":"Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation","date":"2019-11-18","arxiv_id":"1911.07450","repositories_listed":0,"syntology":null},{"url":null,"slug":"2nd-place-solution-in-google-ai-open-images","title":"2nd Place Solution in Google AI Open Images Object Detection Track 2019","date":"2019-11-17","arxiv_id":"1911.07171","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multi-view-image-sets-for","title":"Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation","date":"2019-11-17","arxiv_id":"1911.07262","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-visual-representation-learning-2","title":"Unsupervised Visual Representation Learning with Increasing Object Shape Bias","date":"2019-11-17","arxiv_id":"1911.07272","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-object-localization-with-2","title":"Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation","date":"2019-11-17","arxiv_id":"1911.07160","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-self-paced-learning-for-cross","title":"Curriculum Self-Paced Learning for Cross-Domain Object Detection","date":"2019-11-15","arxiv_id":"1911.06849","repositories_listed":0,"syntology":null},{"url":null,"slug":"pi-rcnn-an-efficient-multi-sensor-3d-object","title":"PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module","date":"2019-11-14","arxiv_id":"1911.06084","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-centric-task-and-motion-planning-in","title":"Object-Centric Task and Motion Planning in Dynamic Environments","date":"2019-11-12","arxiv_id":"1911.04679","repositories_listed":0,"syntology":null},{"url":null,"slug":"pose-estimation-and-bin-picking-for","title":"Pose estimation and bin picking for deformable products","date":"2019-11-12","arxiv_id":"1911.05185","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-hierarchical-tensor","title":"Compositional Hierarchical Tensor Factorization: Representing Hierarchical Intrinsic and Extrinsic Causal Factors","date":"2019-11-11","arxiv_id":"1911.04180","repositories_listed":0,"syntology":null},{"url":null,"slug":"191104469","title":"A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking","date":"2019-11-09","arxiv_id":"1911.04469","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-annotation-in-single-object","title":"Improving Human Annotation in Single Object Tracking","date":"2019-11-07","arxiv_id":"1911.02807","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-adaption-object-detection-system-for","title":"Model Adaption Object Detection System for Robot","date":"2019-11-07","arxiv_id":"1911.02718","repositories_listed":0,"syntology":null},{"url":null,"slug":"localization-aware-channel-pruning-for-object","title":"Localization-aware Channel Pruning for Object Detection","date":"2019-11-06","arxiv_id":"1911.02237","repositories_listed":0,"syntology":null},{"url":null,"slug":"lapnet-automatic-balanced-loss-and-optimal","title":"LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection","date":"2019-11-04","arxiv_id":"1911.01149","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pretrained-image-classifiers-for","title":"Leveraging Pretrained Image Classifiers for Language-Based Segmentation","date":"2019-11-03","arxiv_id":"1911.00830","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-panoptic-segmentation","title":"Single-Shot Panoptic Segmentation","date":"2019-11-02","arxiv_id":"1911.00764","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-past-references-for-robust","title":"Leveraging Past References for Robust Language Grounding","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/relation-extraction-among-multiple-entities","slug":"relation-extraction-among-multiple-entities","title":"Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-methods-for-textureless-object","title":"A Review of methods for Textureless Object Recognition","date":"2019-10-31","arxiv_id":"1910.14255","repositories_listed":0,"syntology":null},{"url":null,"slug":"modified-u-net-mu-net-with-incorporation-of","title":"Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images","date":"2019-10-31","arxiv_id":"1911.00140","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-oriented-state-editing-for-hrl","title":"Object-oriented state editing for HRL","date":"2019-10-31","arxiv_id":"1910.14361","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-interaction-between-deep-detectors-and","title":"On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance","date":"2019-10-31","arxiv_id":"1910.14552","repositories_listed":0,"syntology":null},{"url":null,"slug":"191013348","title":"Sequential image processing methods for improving semantic video segmentation algorithms","date":"2019-10-29","arxiv_id":"1910.13348","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-and-consistent-multi-image","title":"Distributed and Consistent Multi-Image Feature Matching via QuickMatch","date":"2019-10-29","arxiv_id":"1910.13317","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-set-affect-on-super-resolution-for","title":"Training Set Effect on Super Resolution for Automated Target Recognition","date":"2019-10-29","arxiv_id":"1911.07934","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-sim2real-gap-in-robotic-3d","title":"Addressing the Sim2Real Gap in Robotic 3D Object Classification","date":"2019-10-28","arxiv_id":"1910.12585","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-object-detection-over-scientific","title":"Fine-Grained Object Detection over Scientific Document Images with Region Embeddings","date":"2019-10-28","arxiv_id":"1910.12462","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-task-oriented-grasping-from-human","title":"Learning Task-Oriented Grasping from Human Activity Datasets","date":"2019-10-25","arxiv_id":"1910.11669","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-track-any-object","title":"Learning to Track Any Object","date":"2019-10-25","arxiv_id":"1910.11844","repositories_listed":0,"syntology":null},{"url":null,"slug":"team-pfdets-methods-for-open-images-challenge","title":"Team PFDet's Methods for Open Images Challenge 2019","date":"2019-10-25","arxiv_id":"1910.11534","repositories_listed":0,"syntology":null},{"url":null,"slug":"aggregation-signature-for-small-object","title":"Aggregation Signature for Small Object Tracking","date":"2019-10-24","arxiv_id":"1910.10859","repositories_listed":0,"syntology":null},{"url":null,"slug":"robo-robust-fully-neural-object-detection-for","title":"ROBO: Robust, Fully Neural Object Detection for Robot Soccer","date":"2019-10-24","arxiv_id":"1910.10949","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-6d-object-pose-estimation-by-pose","title":"Accurate 6D Object Pose Estimation by Pose Conditioned Mesh Reconstruction","date":"2019-10-23","arxiv_id":"1910.10653","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-power-end-to-end-hybrid-neuromorphic","title":"A low-power end-to-end hybrid neuromorphic framework for surveillance applications","date":"2019-10-22","arxiv_id":"1910.09806","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-robust-disentangling-of-latent-1","title":"Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis","date":"2019-10-22","arxiv_id":"1910.10223","repositories_listed":0,"syntology":null},{"url":null,"slug":"cpwc-contextual-point-wise-convolution-for","title":"CPWC: Contextual Point Wise Convolution for Object Recognition","date":"2019-10-21","arxiv_id":"1910.09643","repositories_listed":0,"syntology":null},{"url":null,"slug":"endowing-deep-3d-models-with-rotation","title":"Endowing Deep 3D Models with Rotation Invariance Based on Principal Component Analysis","date":"2019-10-20","arxiv_id":"1910.08901","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-6d-multi-object-pose-estimation-in","title":"Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning","date":"2019-10-19","arxiv_id":"1910.08811","repositories_listed":0,"syntology":null},{"url":null,"slug":"eye-in-the-sky-drone-based-object-tracking","title":"Eye in the Sky: Drone-Based Object Tracking and 3D Localization","date":"2019-10-18","arxiv_id":"1910.08259","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-contextual-attention-for-human-object","title":"Deep Contextual Attention for Human-Object Interaction Detection","date":"2019-10-17","arxiv_id":"1910.07721","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoder-decoder-based-cnn-and-fully-connected","title":"Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation","date":"2019-10-14","arxiv_id":"1910.06041","repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-in-my-room-object-recognition-on-indoor","title":"What's in my Room? Object Recognition on Indoor Panoramic Images","date":"2019-10-14","arxiv_id":"1910.06138","repositories_listed":0,"syntology":null},{"url":null,"slug":"slope-difference-distribution-and-its","title":"Contour Sparse Representation with SDD Features for Object Recognition","date":"2019-10-13","arxiv_id":"1910.05704","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-application-of-a-deep-learning-algorithm","title":"An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions in tunnels","date":"2019-10-11","arxiv_id":"1910.11094","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-sqair-relational-sequential-attend-infer","title":"R-SQAIR: Relational Sequential Attend, Infer, Repeat","date":"2019-10-11","arxiv_id":"1910.05231","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-induced-deep-q-network-for-a-slide","title":"Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures","date":"2019-10-09","arxiv_id":"1910.03781","repositories_listed":0,"syntology":null},{"url":"/paper/patch-refinement-localized-3d-object","slug":"patch-refinement-localized-3d-object","title":"Patch Refinement -- Localized 3D Object Detection","date":"2019-10-09","arxiv_id":"1910.04093","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-6d-object-pose-predictions-using","title":"Refining 6D Object Pose Predictions using Abstract Render-and-Compare","date":"2019-10-08","arxiv_id":"1910.03412","repositories_listed":0,"syntology":null},{"url":null,"slug":"xyolo-a-model-for-real-time-object-detection","title":"xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware","date":"2019-10-08","arxiv_id":"1910.03159","repositories_listed":0,"syntology":null},{"url":"/paper/label-penet-sequential-label-propagation-and","slug":"label-penet-sequential-label-propagation-and","title":"Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation","date":"2019-10-07","arxiv_id":"1910.02624","repositories_listed":0,"syntology":null},{"url":null,"slug":"colored-transparent-object-matting-from-a","title":"Colored Transparent Object Matting from a Single Image Using Deep Learning","date":"2019-10-05","arxiv_id":"1910.02222","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-estimation-of-users-intention-of-tele","title":"Early Estimation of User's Intention of Tele-Operation Using Object Affordance and Hand Motion in a Dual First-Person Vision","date":"2019-10-05","arxiv_id":"1910.02201","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-segmentation-tracking-from-generic","title":"Object Segmentation Tracking from Generic Video Cues","date":"2019-10-05","arxiv_id":"1910.02258","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-with-weak-supervision-for","title":"Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops","date":"2019-10-04","arxiv_id":"1910.01789","repositories_listed":0,"syntology":null},{"url":"/paper/adaptively-denoising-proposal-collection","slug":"adaptively-denoising-proposal-collection","title":"Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization","date":"2019-10-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptively-denoising-proposal-collection-for","title":"Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization","date":"2019-10-04","arxiv_id":"1910.02101","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-function-networks-for-view","title":"Higher Order Function Networks for View Planning and Multi-View Reconstruction","date":"2019-10-04","arxiv_id":"1910.02066","repositories_listed":0,"syntology":null},{"url":null,"slug":"360-indoor-towards-learning-real-world","title":"360-Indoor: Towards Learning Real-World Objects in 360° Indoor Equirectangular Images","date":"2019-10-03","arxiv_id":"1910.01712","repositories_listed":0,"syntology":null},{"url":null,"slug":"kidney-recognition-in-ct-using-yolov3","title":"Kidney Recognition in CT Using YOLOv3","date":"2019-10-03","arxiv_id":"1910.01268","repositories_listed":0,"syntology":null}],"record_sha256":"700a885cf607b2137ee2a629346636ea1167b7944a4adf16b9e6617d6d1ec4be","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}