{"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/104","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":104,"pages_in_order":110,"rows_per_page":100,"rows":[10301,10400],"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/103","next":"/task/object-detection/papers/105","papers":[{"url":null,"slug":"extend-the-shallow-part-of-single-shot","title":"Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network","date":"2018-01-18","arxiv_id":"1801.05918","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-accurate-and-real-time-self-blast-glass","title":"An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images","date":"2018-01-16","arxiv_id":"1801.05143","repositories_listed":0,"syntology":null},{"url":null,"slug":"localization-aware-active-learning-for-object","title":"Localization-Aware Active Learning for Object Detection","date":"2018-01-16","arxiv_id":"1801.05124","repositories_listed":0,"syntology":null},{"url":null,"slug":"stressednets-efficient-feature","title":"StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks","date":"2018-01-16","arxiv_id":"1801.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-octree-cells-occupancy-geometric","title":"An octree cells occupancy geometric dimensionality descriptor for massive on-server point cloud visualisation and classification","date":"2018-01-15","arxiv_id":"1801.05038","repositories_listed":0,"syntology":null},{"url":null,"slug":"msdnn-multi-scale-deep-neural-network-for","title":"MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection","date":"2018-01-12","arxiv_id":"1801.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-superpixel-to-human-shape-modelling-for","title":"From Superpixel to Human Shape Modelling for Carried Object Detection","date":"2018-01-10","arxiv_id":"1801.03551","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-and-semantic-knowledge-transfer-for","title":"Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection","date":"2018-01-09","arxiv_id":"1801.03145","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-object-detection-and-tracking","title":"Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties","date":"2018-01-08","arxiv_id":"1801.02686","repositories_listed":0,"syntology":null},{"url":null,"slug":"remotenet-efficient-relevant-motion-event","title":"ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos","date":"2018-01-06","arxiv_id":"1801.02031","repositories_listed":0,"syntology":null},{"url":"/paper/3d-detnet-a-single-stage-video-based-vehicle","slug":"3d-detnet-a-single-stage-video-based-vehicle","title":"3D-DETNet: a Single Stage Video-Based Vehicle Detector","date":"2018-01-05","arxiv_id":"1801.01769","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-segmentation-in-depth-maps-with-one","title":"Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network","date":"2018-01-04","arxiv_id":"1801.01281","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robot-vision-module-development-with","title":"TOWARDS ROBOT VISION MODULE DEVELOPMENT WITH EXPERIENTIAL ROBOT LEARNING","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-deep-salient-object","title":"Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels","date":"2017-12-27","arxiv_id":"1712.09558","repositories_listed":0,"syntology":null},{"url":null,"slug":"lost-in-time-temporal-analytics-for-long-term","title":"Lost in Time: Temporal Analytics for Long-Term Video Surveillance","date":"2017-12-20","arxiv_id":"1712.07322","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fixation-point-strategy-for-object","title":"Learning Fixation Point Strategy for Object Detection and Classification","date":"2017-12-19","arxiv_id":"1712.06897","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-objects-using-3d-object-proposals","title":"Tracking objects using 3D object proposals","date":"2017-12-19","arxiv_id":"1712.06780","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-flow-for-compressing-convolution","title":"Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA","date":"2017-12-18","arxiv_id":"1712.06272","repositories_listed":0,"syntology":null},{"url":null,"slug":"impression-network-for-video-object-detection","title":"Impression Network for Video Object Detection","date":"2017-12-16","arxiv_id":"1712.05896","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-and-attention-diagnosing-pulmonary","title":"Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians","date":"2017-12-14","arxiv_id":"1712.05114","repositories_listed":0,"syntology":null},{"url":"/paper/masklab-instance-segmentation-by-refining","slug":"masklab-instance-segmentation-by-refining","title":"MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features","date":"2017-12-13","arxiv_id":"1712.04837","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbol-detection-in-online-handwritten","title":"Symbol detection in online handwritten graphics using Faster R-CNN","date":"2017-12-13","arxiv_id":"1712.04833","repositories_listed":0,"syntology":null},{"url":null,"slug":"weaving-multi-scale-context-for-single-shot","title":"Weaving Multi-scale Context for Single Shot Detector","date":"2017-12-08","arxiv_id":"1712.03149","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-regionlets-for-object-detection","title":"Deep Regionlets for Object Detection","date":"2017-12-06","arxiv_id":"1712.02408","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-recognition-of-coal-and-gangue","title":"Automatic Recognition of Coal and Gangue based on Convolution Neural Network","date":"2017-12-03","arxiv_id":"1712.00720","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-efficient-object-detection-models","title":"Learning Efficient Object Detection Models with Knowledge Distillation","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rank-of-experts-detection-network-ensemble","title":"Rank of Experts: Detection Network Ensemble","date":"2017-12-01","arxiv_id":"1712.00185","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-object-detection-with-enriched","title":"Single-Shot Object Detection with Enriched Semantics","date":"2017-12-01","arxiv_id":"1712.00433","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-graph-structure-for-salient-object","title":"A novel graph structure for salient object detection based on divergence background and compact foreground","date":"2017-11-30","arxiv_id":"1711.11266","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-cnn-based-object-detection-for","title":"Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness","date":"2017-11-30","arxiv_id":"1712.00075","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-high-performance-video-object-1","title":"Towards High Performance Video Object Detection","date":"2017-11-30","arxiv_id":"1711.11577","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-learning-of-open-vocabulary","title":"Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation","date":"2017-11-27","arxiv_id":"1711.09509","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-object-detection-for-stylized","title":"Scalable Object Detection for Stylized Objects","date":"2017-11-27","arxiv_id":"1711.09822","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-instance-curriculum-learning-for","title":"Multiple Instance Curriculum Learning for Weakly Supervised Object Detection","date":"2017-11-25","arxiv_id":"1711.09191","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selective-networks-for-object","title":"Feature Selective Networks for Object Detection","date":"2017-11-24","arxiv_id":"1711.08879","repositories_listed":0,"syntology":null},{"url":"/paper/an-analysis-of-scale-invariance-in-object-1","slug":"an-analysis-of-scale-invariance-in-object-1","title":"An Analysis of Scale Invariance in Object Detection - SNIP","date":"2017-11-22","arxiv_id":"1711.08189","repositories_listed":0,"syntology":null},{"url":"/paper/weakly-supervised-object-discovery-by","slug":"weakly-supervised-object-discovery-by","title":"Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks","date":"2017-11-22","arxiv_id":"1711.08174","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-bird-view-lidar-point-cloud-and-front","title":"Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection","date":"2017-11-17","arxiv_id":"1711.06703","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-annotation-object-detection-with-web","title":"Zero-Annotation Object Detection with Web Knowledge Transfer","date":"2017-11-16","arxiv_id":"1711.05954","repositories_listed":0,"syntology":null},{"url":null,"slug":"apprentice-using-knowledge-distillation","title":"Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy","date":"2017-11-15","arxiv_id":"1711.05852","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-object-detection-with-a-few","title":"Contextual Object Detection with a Few Relevant Neighbors","date":"2017-11-15","arxiv_id":"1711.05705","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-zoom-in-network-for-fast-object","title":"Dynamic Zoom-in Network for Fast Object Detection in Large Images","date":"2017-11-14","arxiv_id":"1711.05187","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-enhancement-network-a-refined-scene","title":"Feature Enhancement Network: A Refined Scene Text Detector","date":"2017-11-12","arxiv_id":"1711.04249","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-residual-text-detection-network-for","title":"Deep Residual Text Detection Network for Scene Text","date":"2017-11-11","arxiv_id":"1711.04147","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-hardware-implementations-of","title":"A Survey on Hardware Implementations of Visual Object Trackers","date":"2017-11-07","arxiv_id":"1711.02441","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-joint-3d-2d-based-method-for-free-space","title":"A Joint 3D-2D based Method for Free Space Detection on Roads","date":"2017-11-06","arxiv_id":"1711.02144","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-pyramid-context-aware-moving-object","title":"Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery","date":"2017-11-05","arxiv_id":"1711.01656","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-taught-obesrve-ask-toa-method-for-object","title":"A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision","date":"2017-11-03","arxiv_id":"1711.01043","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-ssd-learning-hierarchical-features-from","title":"3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection","date":"2017-11-01","arxiv_id":"1711.00238","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-textures-with-mobile-cameras-for","title":"Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications","date":"2017-11-01","arxiv_id":"1711.00558","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-saliency-detection-via-fusing","title":"Robust Saliency Detection via Fusing Foreground and Background Priors","date":"2017-11-01","arxiv_id":"1711.00322","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multitask-deep-learning-model-for-real-time","title":"A multitask deep learning model for real-time deployment in embedded systems","date":"2017-10-31","arxiv_id":"1711.00146","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-patch-matching-using-convolutional","title":"Image Patch Matching Using Convolutional Descriptors with Euclidean Distance","date":"2017-10-31","arxiv_id":"1710.11359","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-supervised-stdp-based-training-algorithm","title":"A Supervised STDP-based Training Algorithm for Living Neural Networks","date":"2017-10-30","arxiv_id":"1710.10944","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-region-proposal-and-global-context","title":"Cascade Region Proposal and Global Context for Deep Object Detection","date":"2017-10-30","arxiv_id":"1710.10749","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-efficiency-compression-for-object","title":"High efficiency compression for object detection","date":"2017-10-30","arxiv_id":"1710.11151","repositories_listed":0,"syntology":null},{"url":null,"slug":"seethrough-finding-chairs-in-heavily-occluded","title":"SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images","date":"2017-10-28","arxiv_id":"1710.10473","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-6d-pose-estimation-of-objects-in","title":"Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search","date":"2017-10-24","arxiv_id":"1710.08577","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-feature-collection-for","title":"Investigating the feature collection for semantic segmentation via single skip connection","date":"2017-10-23","arxiv_id":"1710.08192","repositories_listed":0,"syntology":null},{"url":null,"slug":"ada-a-game-theoretic-perspective-on-data","title":"ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection","date":"2017-10-21","arxiv_id":"1710.07735","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-guided-black-box-safety-testing-of","title":"Feature-Guided Black-Box Safety Testing of Deep Neural Networks","date":"2017-10-21","arxiv_id":"1710.07859","repositories_listed":0,"syntology":null},{"url":null,"slug":"dropout-sampling-for-robust-object-detection","title":"Dropout Sampling for Robust Object Detection in Open-Set Conditions","date":"2017-10-18","arxiv_id":"1710.06677","repositories_listed":0,"syntology":null},{"url":null,"slug":"fishing-for-clickbaits-in-social-images-and","title":"Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models","date":"2017-10-17","arxiv_id":"1710.06390","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-salient-object-detection-for","title":"Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model","date":"2017-10-10","arxiv_id":"1710.04071","repositories_listed":0,"syntology":null},{"url":"/paper/2d-driven-3d-object-detection-in-rgb-d-images","slug":"2d-driven-3d-object-detection-in-rgb-d-images","title":"2D-Driven 3D Object Detection in RGB-D Images","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chained-cascade-network-for-object-detection","title":"Chained Cascade Network for Object Detection","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"delving-into-salient-object-subitizing-and","title":"Delving Into Salient Object Subitizing and Detection","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"look-perceive-and-segment-finding-the-salient","title":"Look, Perceive and Segment: Finding the Salient Objects in Images via Two-Stream Fixation-Semantic CNNs","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"moving-object-detection-in-time-lapse-or","title":"Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-enhancement-for-detection-of-multiple","title":"Mutual Enhancement for Detection of Multiple Logos in Sports Videos","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-level-proposals","title":"Object-Level Proposals","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-video-object-detection-using","title":"Online Video Object Detection Using Association LSTM","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scenenet-rgb-d-can-5m-synthetic-images-beat","title":"SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervision-by-fusion-towards-unsupervised","title":"Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-unified-compositional-model-for","title":"Towards a Unified Compositional Model for Visual Pattern Modeling","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-labels-for-supervised-learning-on","title":"Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion","date":"2017-09-27","arxiv_id":"1709.09323","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-from-synthesis-to-reality","title":"Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection","date":"2017-09-24","arxiv_id":"1709.08142","repositories_listed":0,"syntology":null},{"url":"/paper/playing-for-benchmarks","slug":"playing-for-benchmarks","title":"Playing for Benchmarks","date":"2017-09-21","arxiv_id":"1709.07322","repositories_listed":0,"syntology":null},{"url":null,"slug":"clickbait-click-based-accelerated-incremental","title":"ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks","date":"2017-09-15","arxiv_id":"1709.05021","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiating-objects-by-motion-joint","title":"Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects","date":"2017-09-14","arxiv_id":"1709.04666","repositories_listed":0,"syntology":null},{"url":null,"slug":"modnet-moving-object-detection-network-with","title":"MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving","date":"2017-09-14","arxiv_id":"1709.04821","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-ground-truths-projected-image","title":"Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision","date":"2017-09-12","arxiv_id":"1709.03697","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-region-based-densely-connected","title":"Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application","date":"2017-09-12","arxiv_id":"1709.07943","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-united-video-dehazing-and","title":"End-to-End United Video Dehazing and Detection","date":"2017-09-12","arxiv_id":"1709.03919","repositories_listed":0,"syntology":null},{"url":"/paper/clad-a-complex-and-long-activities-dataset","slug":"clad-a-complex-and-long-activities-dataset","title":"CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations","date":"2017-09-11","arxiv_id":"1709.03456","repositories_listed":0,"syntology":null},{"url":"/paper/fused-text-segmentation-networks-for-multi","slug":"fused-text-segmentation-networks-for-multi","title":"Fused Text Segmentation Networks for Multi-oriented Scene Text Detection","date":"2017-09-11","arxiv_id":"1709.03272","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-neural-networks-for","title":"Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps","date":"2017-09-10","arxiv_id":"1709.03139","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-neural-networks-for-1","title":"Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)","date":"2017-09-10","arxiv_id":"1709.03138","repositories_listed":0,"syntology":null},{"url":null,"slug":"locating-3d-object-proposals-a-depth-based","title":"Locating 3D Object Proposals: A Depth-Based Online Approach","date":"2017-09-08","arxiv_id":"1709.02653","repositories_listed":0,"syntology":null},{"url":null,"slug":"objectness-scoring-and-detection-proposals-in","title":"Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks","date":"2017-09-08","arxiv_id":"1709.02600","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sonar-image-patch-matching-via-deep","title":"Improving Sonar Image Patch Matching via Deep Learning","date":"2017-09-07","arxiv_id":"1709.02150","repositories_listed":0,"syntology":null},{"url":null,"slug":"6d-object-pose-estimation-with-depth-images-a","title":"6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality","date":"2017-09-05","arxiv_id":"1709.01459","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-moving-object-in-dynamic","title":"Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA","date":"2017-09-03","arxiv_id":"1709.00657","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-filter-in-crf-based-semantic","title":"Gaussian Filter in CRF Based Semantic Segmentation","date":"2017-09-02","arxiv_id":"1709.00516","repositories_listed":0,"syntology":null},{"url":"/paper/semantic-foggy-scene-understanding-with","slug":"semantic-foggy-scene-understanding-with","title":"Semantic Foggy Scene Understanding with Synthetic Data","date":"2017-08-25","arxiv_id":"1708.07819","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparcnn-spatially-related-convolutional","title":"SPARCNN: SPAtially Related Convolutional Neural Networks","date":"2017-08-24","arxiv_id":"1708.07522","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-search-of-inliers-3d-correspondence-by","title":"In search of inliers: 3d correspondence by local and global voting","date":"2017-08-23","arxiv_id":"1708.06966","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-kernel-based-vehicle-tracking-using","title":"Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration","date":"2017-08-22","arxiv_id":"1708.06831","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automatic-construction-of-diverse","title":"Towards Automatic Construction of Diverse, High-quality Image Dataset","date":"2017-08-22","arxiv_id":"1708.06495","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-explanatory-deep-salient-object","title":"Self-explanatory Deep Salient Object Detection","date":"2017-08-18","arxiv_id":"1708.05595","repositories_listed":0,"syntology":null}],"record_sha256":"235bdbd89749024c4ffa745852098ebaf3c169efbbe6c45791c8dffc4e00f1d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}