{"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/88","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":88,"pages_in_order":106,"rows_per_page":100,"rows":[8701,8800],"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/87","next":"/task/object-detection-1/papers/89","papers":[{"url":null,"slug":"demystifying-contrastive-self-supervised","title":"Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases","date":"2020-07-28","arxiv_id":"2007.13916","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-lossy-image-and-video","title":"On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures","date":"2020-07-28","arxiv_id":"2007.14314","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-soft-qubo-suppression-for-accurate","title":"Quantum-soft QUBO Suppression for Accurate Object Detection","date":"2020-07-28","arxiv_id":"2007.13992","repositories_listed":0,"syntology":null},{"url":null,"slug":"radarnet-exploiting-radar-for-robust","title":"RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects","date":"2020-07-28","arxiv_id":"2007.14366","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-averse-mpc-via-visual-inertial-input-and","title":"Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance","date":"2020-07-28","arxiv_id":"2007.14035","repositories_listed":0,"syntology":null},{"url":null,"slug":"radio-access-technology-characterisation","title":"Radio Access Technology Characterisation Through Object Detection","date":"2020-07-27","arxiv_id":"2007.13561","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-progress-of-convolutional-neural","title":"Research Progress of Convolutional Neural Network and its Application in Object Detection","date":"2020-07-27","arxiv_id":"2007.13284","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-level-residual-distillation-based-triple","title":"Two-Level Residual Distillation based Triple Network for Incremental Object Detection","date":"2020-07-27","arxiv_id":"2007.13428","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-lstm-approach-to-temporal-3d-object","title":"An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds","date":"2020-07-24","arxiv_id":"2007.12392","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-evaluation-standard-for-automatic","title":"A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal","date":"2020-07-23","arxiv_id":"2007.12082","repositories_listed":0,"syntology":null},{"url":null,"slug":"right-for-the-right-reason-making-image","title":"Right for the Right Reason: Making Image Classification Robust","date":"2020-07-23","arxiv_id":"2007.11924","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-deep-learning-applications-in","title":"Accelerating Deep Learning Applications in Space","date":"2020-07-21","arxiv_id":"2007.11089","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-automated-segmentation-of-the-left","title":"Segmentation of the Left Ventricle by SDD double threshold selection and CHT","date":"2020-07-21","arxiv_id":"2007.10665","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-aware-centroid-voting-for-monocular-3d","title":"Object-Aware Centroid Voting for Monocular 3D Object Detection","date":"2020-07-20","arxiv_id":"2007.09836","repositories_listed":0,"syntology":null},{"url":null,"slug":"tenet-triple-excitation-network-for-video","title":"TENet: Triple Excitation Network for Video Salient Object Detection","date":"2020-07-20","arxiv_id":"2007.09943","repositories_listed":0,"syntology":null},{"url":null,"slug":"referring-expression-comprehension-a-survey","title":"Referring Expression Comprehension: A Survey of Methods and Datasets","date":"2020-07-19","arxiv_id":"2007.09554","repositories_listed":0,"syntology":null},{"url":null,"slug":"aabo-adaptive-anchor-box-optimization-for","title":"AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling","date":"2020-07-18","arxiv_id":"2007.09336","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-pyramid-transformer","title":"Feature Pyramid Transformer","date":"2020-07-18","arxiv_id":"2007.09451","repositories_listed":0,"syntology":null},{"url":null,"slug":"2nd-place-solution-to-eccv-2020-vipriors","title":"2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge","date":"2020-07-17","arxiv_id":"2007.08849","repositories_listed":0,"syntology":null},{"url":null,"slug":"casnet-common-attribute-support-network-for","title":"CASNet: Common Attribute Support Network for image instance and panoptic segmentation","date":"2020-07-17","arxiv_id":"2008.00810","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-object-detection-with-selective","title":"Improving Object Detection with Selective Self-supervised Self-training","date":"2020-07-17","arxiv_id":"2007.09162","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-in-deep-learning-for-hyperspectral","title":"Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios","date":"2020-07-16","arxiv_id":"2007.08592","repositories_listed":0,"syntology":null},{"url":"/paper/infofocus-3d-object-detection-for-autonomous","slug":"infofocus-3d-object-detection-for-autonomous","title":"InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling","date":"2020-07-16","arxiv_id":"2007.08556","repositories_listed":0,"syntology":null},{"url":"/paper/trashcan-a-semantically-segmented-dataset","slug":"trashcan-a-semantically-segmented-dataset","title":"TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris","date":"2020-07-16","arxiv_id":"2007.08097","repositories_listed":0,"syntology":null},{"url":null,"slug":"vipriors-object-detection-challenge","title":"VIPriors Object Detection Challenge","date":"2020-07-16","arxiv_id":"2007.08170","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-cnn-based-object-classifier-using","title":"Decoding CNN based Object Classifier Using Visualization","date":"2020-07-15","arxiv_id":"2007.07482","repositories_listed":0,"syntology":null},{"url":null,"slug":"dive-deeper-into-box-for-object-detection","title":"Dive Deeper Into Box for Object Detection","date":"2020-07-15","arxiv_id":"2007.14350","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-extraction-of-road-intersection","title":"Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks","date":"2020-07-14","arxiv_id":"2007.07404","repositories_listed":0,"syntology":null},{"url":null,"slug":"cobe-contextualized-object-embeddings-from","title":"COBE: Contextualized Object Embeddings from Narrated Instructional Video","date":"2020-07-14","arxiv_id":"2007.07306","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-polynomial-and","title":"Comparative Analysis of Polynomial and Rational Approximations of Hyperbolic Tangent Function for VLSI Implementation","date":"2020-07-13","arxiv_id":"2007.11976","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-multi-object-tracking-with-global","title":"End-to-End Multi-Object Tracking with Global Response Map","date":"2020-07-13","arxiv_id":"2007.06344","repositories_listed":0,"syntology":null},{"url":null,"slug":"location-aware-box-reasoning-for-anchor-based","title":"Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection","date":"2020-07-13","arxiv_id":"2007.06233","repositories_listed":0,"syntology":null},{"url":"/paper/eagle-large-scale-dataset-for-vehicle","slug":"eagle-large-scale-dataset-for-vehicle","title":"EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery","date":"2020-07-12","arxiv_id":"2007.06124","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-associate-detections-for-real","title":"Learning to associate detections for real-time multiple object tracking","date":"2020-07-12","arxiv_id":"2007.06041","repositories_listed":0,"syntology":null},{"url":null,"slug":"vinnas-variational-inference-based-neural","title":"VINNAS: Variational Inference-based Neural Network Architecture Search","date":"2020-07-12","arxiv_id":"2007.06103","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-resource-management-in-uavs-for","title":"Efficient resource management in UAVs for Visual Assistance","date":"2020-07-11","arxiv_id":"2007.05854","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-detection-through-wavelet-transforms-in","title":"Cloud Detection through Wavelet Transforms in Machine Learning and Deep Learning","date":"2020-07-10","arxiv_id":"2007.13678","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-motion-artifact-reduction-on-brain","title":"Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques","date":"2020-07-10","arxiv_id":"2007.05149","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-cross-modal-interaction-network","title":"Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection","date":"2020-07-10","arxiv_id":"2007.14352","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-robust-industrial-applicable-object","title":"Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures","date":"2020-07-09","arxiv_id":"2007.04666","repositories_listed":0,"syntology":null},{"url":null,"slug":"pollen13k-a-large-scale-microscope-pollen","title":"Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset","date":"2020-07-09","arxiv_id":"2007.04690","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quick-review-on-recent-trends-in-3d-point","title":"A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain","date":"2020-07-08","arxiv_id":"2007.05038","repositories_listed":0,"syntology":null},{"url":null,"slug":"nasgem-neural-architecture-search-via-graph","title":"NASGEM: Neural Architecture Search via Graph Embedding Method","date":"2020-07-08","arxiv_id":"2007.04452","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrated-batchnorm-improving-robustness","title":"Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization","date":"2020-07-07","arxiv_id":"2007.03230","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-apple-diseases","title":"Deep Learning for Apple Diseases: Classification and Identification","date":"2020-07-06","arxiv_id":"2007.02980","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-domain-classifier-bank-for","title":"Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection","date":"2020-07-06","arxiv_id":"2007.02595","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-evaluation-of-object-detection","title":"A Systematic Evaluation of Object Detection Networks for Scientific Plots","date":"2020-07-05","arxiv_id":"2007.02240","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-grid-rendering-networks-for-3d-object","title":"Local Grid Rendering Networks for 3D Object Detection in Point Clouds","date":"2020-07-04","arxiv_id":"2007.02099","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptive-object-detection-via","title":"Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN","date":"2020-07-03","arxiv_id":"2007.01571","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-regions-of-interest-in-large-multi","title":"Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology","date":"2020-07-03","arxiv_id":"2007.01866","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-models-for-visual-inspection-on","title":"Deep Learning Models for Visual Inspection on Automotive Assembling Line","date":"2020-07-02","arxiv_id":"2007.01857","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-bounding-box-annotation-for-object","title":"Iterative Bounding Box Annotation for Object Detection","date":"2020-07-02","arxiv_id":"2007.00961","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-crack-detection-on-road-pavements","title":"Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture","date":"2020-07-01","arxiv_id":"2007.00477","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimisation-of-the-pointpillars-network-for","title":"Optimisation of the PointPillars network for 3D object detection in point clouds","date":"2020-07-01","arxiv_id":"2007.00493","repositories_listed":0,"syntology":null},{"url":null,"slug":"tiledsoilingnet-tile-level-soiling-detection","title":"TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric","date":"2020-07-01","arxiv_id":"2007.00801","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-deep-neural-networks-with","title":"Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey","date":"2020-06-30","arxiv_id":"2006.16867","repositories_listed":0,"syntology":null},{"url":"/paper/can-your-face-detector-do-anti-spoofing-face","slug":"can-your-face-detector-do-anti-spoofing-face","title":"Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector","date":"2020-06-30","arxiv_id":"2006.16836","repositories_listed":0,"syntology":null},{"url":null,"slug":"fathomnet-an-underwater-image-training","title":"FathomNet: An underwater image training database for ocean exploration and discovery","date":"2020-06-30","arxiv_id":"2007.00114","repositories_listed":0,"syntology":null},{"url":null,"slug":"itself-iterative-saliency-estimation-flexible","title":"ITSELF: Iterative Saliency Estimation fLexible Framework","date":"2020-06-30","arxiv_id":"2006.16956","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-under-rainy-conditions-for","title":"Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques","date":"2020-06-30","arxiv_id":"2006.16471","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-benchmark-dataset-for-both-underwater-image","title":"A Benchmark dataset for both underwater image enhancement and underwater object detection","date":"2020-06-29","arxiv_id":"2006.15789","repositories_listed":0,"syntology":null},{"url":null,"slug":"1st-place-solution-for-waymo-open-dataset","title":"1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation","date":"2020-06-28","arxiv_id":"2006.15505","repositories_listed":0,"syntology":null},{"url":null,"slug":"2nd-place-solution-for-waymo-open-dataset","title":"2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection","date":"2020-06-28","arxiv_id":"2006.15507","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-instance-segmentation-state-of","title":"A Survey on Instance Segmentation: State of the art","date":"2020-06-28","arxiv_id":"2007.00047","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-convolutional-neural-networks-a-2","title":"Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion","date":"2020-06-28","arxiv_id":"2006.15538","repositories_listed":0,"syntology":null},{"url":null,"slug":"dhari-report-to-epic-kitchens-2020-object","title":"DHARI Report to EPIC-Kitchens 2020 Object Detection Challenge","date":"2020-06-28","arxiv_id":"2006.15553","repositories_listed":0,"syntology":null},{"url":"/paper/localization-uncertainty-estimation-for","slug":"localization-uncertainty-estimation-for","title":"Localization Uncertainty Estimation for Anchor-Free Object Detection","date":"2020-06-28","arxiv_id":"2006.15607","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-ssupervised-object-detection","title":"Cross-Supervised Object Detection","date":"2020-06-26","arxiv_id":"2006.15056","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-contrast-for-domain-adaptive-object","title":"Domain Contrast for Domain Adaptive Object Detection","date":"2020-06-26","arxiv_id":"2006.14863","repositories_listed":0,"syntology":null},{"url":null,"slug":"expandable-yolo-3d-object-detection-from-rgb","title":"Expandable YOLO: 3D Object Detection from RGB-D Images","date":"2020-06-26","arxiv_id":"2006.14837","repositories_listed":0,"syntology":null},{"url":null,"slug":"road-obstacles-positional-and-dynamic","title":"Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data","date":"2020-06-24","arxiv_id":"2006.14011","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-regularized-building-footprint","title":"Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network","date":"2020-06-23","arxiv_id":"2006.13176","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-regularized-building-footprint-1","title":"BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS","date":"2020-06-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-3d-detection-of-vehicles-from","title":"Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time","date":"2020-06-23","arxiv_id":"2006.13084","repositories_listed":0,"syntology":null},{"url":null,"slug":"slv-spatial-likelihood-voting-for-weakly-1","title":"SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection","date":"2020-06-23","arxiv_id":"2006.12884","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-sensor-fusion-in-visual","title":"Adversarial Robustness of Deep Sensor Fusion Models","date":"2020-06-23","arxiv_id":"2006.13192","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-rendering-a-survey","title":"Differentiable Rendering: A Survey","date":"2020-06-22","arxiv_id":"2006.12057","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-precision-digital-traffic-recording-with","title":"High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups","date":"2020-06-22","arxiv_id":"2006.12140","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-better-performance-and-more","title":"Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles","date":"2020-06-22","arxiv_id":"2006.12015","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-object-detection-with","title":"Semi-Supervised Object Detection with Sparsely Annotated Dataset","date":"2020-06-21","arxiv_id":"2006.11692","repositories_listed":0,"syntology":null},{"url":null,"slug":"autood-automated-outlier-detection-via","title":"AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning","date":"2020-06-19","arxiv_id":"2006.11321","repositories_listed":0,"syntology":null},{"url":null,"slug":"shop-the-look-building-a-large-scale-visual","title":"Shop The Look: Building a Large Scale Visual Shopping System at Pinterest","date":"2020-06-18","arxiv_id":"2006.10866","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-object-classification-and-meaningful","title":"Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances","date":"2020-06-17","arxiv_id":"2006.10011","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representation-learning-for-1","title":"Self-Supervised Representation Learning for Visual Anomaly Detection","date":"2020-06-17","arxiv_id":"2006.09654","repositories_listed":0,"syntology":null},{"url":null,"slug":"foreground-background-imbalance-problem-in","title":"Foreground-Background Imbalance Problem in Deep Object Detectors: A Review","date":"2020-06-16","arxiv_id":"2006.09238","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-curiosity-for-active-visual-learning","title":"Semantic Curiosity for Active Visual Learning","date":"2020-06-16","arxiv_id":"2006.09367","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-invisibility-detecting-objects","title":"Pixel Invisibility: Detecting Objects Invisible in Color Images","date":"2020-06-15","arxiv_id":"2006.08383","repositories_listed":0,"syntology":null},{"url":null,"slug":"visibility-guided-nms-efficient-boosting-of","title":"Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes","date":"2020-06-15","arxiv_id":"2006.08547","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-object-detection-on-remote-sensing","title":"Few-shot Object Detection on Remote Sensing Images","date":"2020-06-14","arxiv_id":"2006.07826","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyper-rpca-joint-maximum-correntropy","title":"Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection","date":"2020-06-14","arxiv_id":"2006.07795","repositories_listed":0,"syntology":null},{"url":null,"slug":"hrdnet-high-resolution-detection-network-for","title":"HRDNet: High-resolution Detection Network for Small Objects","date":"2020-06-13","arxiv_id":"2006.07607","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-any-shot-object-detection","title":"UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation","date":"2020-06-12","arxiv_id":"2006.07502","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-instance-discrimination-good-for","title":"What makes instance discrimination good for transfer learning?","date":"2020-06-11","arxiv_id":"2006.06606","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-driving-with-deep-learning-a","title":"Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies","date":"2020-06-10","arxiv_id":"2006.06091","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiresolution-attention-extractor-for-small","title":"MultiResolution Attention Extractor for Small Object Detection","date":"2020-06-10","arxiv_id":"2006.05941","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-embedded-deep-learning-object-detection","title":"An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries","date":"2020-06-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-synthetic-data-improve-object-detection","title":"Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?","date":"2020-06-09","arxiv_id":"2006.05015","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-shared-filter-bases-for-efficient","title":"Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks","date":"2020-06-09","arxiv_id":"2006.05066","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvlidarnet-real-time-multi-class-scene","title":"MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views","date":"2020-06-09","arxiv_id":"2006.05518","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-the-shelf-sensor-vs-experimental-radar","title":"Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?","date":"2020-06-09","arxiv_id":"2006.05485","repositories_listed":0,"syntology":null}],"record_sha256":"0b020cb9866da1eaefcc4afa9a6b5bc40ba5e5cd14b0f0c20d411a0b80b4913b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}