{"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/86","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":86,"pages_in_order":106,"rows_per_page":100,"rows":[8501,8600],"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/85","next":"/task/object-detection-1/papers/87","papers":[{"url":null,"slug":"learning-to-predict-the-3d-layout-of-a-scene","title":"Learning to Predict the 3D Layout of a Scene","date":"2020-11-19","arxiv_id":"2011.09977","repositories_listed":0,"syntology":null},{"url":null,"slug":"flaas-federated-learning-as-a-service","title":"FLaaS: Federated Learning as a Service","date":"2020-11-18","arxiv_id":"2011.09359","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-focal-loss-for-class-posterior-probability","title":"On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective","date":"2020-11-18","arxiv_id":"2011.09172","repositories_listed":0,"syntology":null},{"url":null,"slug":"modality-buffet-for-real-time-object","title":"Modality-Buffet for Real-Time Object Detection","date":"2020-11-17","arxiv_id":"2011.08726","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-a-first-meta-survey-of-selected","title":"Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact","date":"2020-11-16","arxiv_id":"2011.08184","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsic-dynamic-sample-individualized-connector","title":"DSIC: Dynamic Sample-Individualized Connector for Multi-Scale Object Detection","date":"2020-11-16","arxiv_id":"2011.07774","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-sharing-and-integration-for","title":"Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors","date":"2020-11-16","arxiv_id":"2011.08317","repositories_listed":0,"syntology":null},{"url":null,"slug":"frdet-balanced-and-lightweight-object","title":"FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving","date":"2020-11-16","arxiv_id":"2011.08061","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-monitoring-of-object-detection","title":"Online Monitoring of Object Detection Performance During Deployment","date":"2020-11-16","arxiv_id":"2011.07750","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-cost-improvements-for-general-object","title":"Zero Cost Improvements for General Object Detection Network","date":"2020-11-16","arxiv_id":"2011.07756","repositories_listed":0,"syntology":null},{"url":null,"slug":"g-rcn-optimizing-the-gap-between","title":"G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection","date":"2020-11-14","arxiv_id":"2012.03677","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-encoder-detector-module-using","title":"Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection","date":"2020-11-13","arxiv_id":"2011.06978","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-uncertainty-quantification-in","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","date":"2020-11-12","arxiv_id":"2011.06225","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-model-compression-by-jointly","title":"Automated Model Compression by Jointly Applied Pruning and Quantization","date":"2020-11-12","arxiv_id":"2011.06231","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-post-intelligent-point-of-sale-and","title":"I-POST: Intelligent Point of Sale and Transaction System","date":"2020-11-12","arxiv_id":"2011.06144","repositories_listed":0,"syntology":null},{"url":null,"slug":"strobe-streaming-object-detection-from-lidar","title":"StrObe: Streaming Object Detection from LiDAR Packets","date":"2020-11-12","arxiv_id":"2011.06425","repositories_listed":0,"syntology":null},{"url":"/paper/learning-from-theodore-a-synthetic","slug":"learning-from-theodore-a-synthetic","title":"Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning","date":"2020-11-11","arxiv_id":"2011.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-segmentation-via-background","title":"Self-supervised Segmentation via Background Inpainting","date":"2020-11-11","arxiv_id":"2011.05626","repositories_listed":0,"syntology":null},{"url":null,"slug":"ellipse-detection-and-localization-with","title":"Ellipse Detection and Localization with Applications to Knots in Sawn Lumber Images","date":"2020-11-10","arxiv_id":"2011.04844","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-communicate-and-correct-pose","title":"Learning to Communicate and Correct Pose Errors","date":"2020-11-10","arxiv_id":"2011.05289","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-language-identification-of-text-in","title":"On-Device Language Identification of Text in Images using Diacritic Characters","date":"2020-11-10","arxiv_id":"2011.05108","repositories_listed":0,"syntology":null},{"url":null,"slug":"closing-the-generalization-gap-in-one-shot-1","title":"A Broad Dataset is All You Need for One-Shot Object Detection","date":"2020-11-09","arxiv_id":"2011.04267","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-object-detection-with-latticed-multi","title":"Fast Object Detection with Latticed Multi-Scale Feature Fusion","date":"2020-11-05","arxiv_id":"2011.02780","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-object-detection-in-real-life-case","title":"Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest","date":"2020-11-05","arxiv_id":"2011.02719","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-vehicle-orientation","title":"Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models","date":"2020-11-05","arxiv_id":"2011.03114","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-fusion-factor-in-fpn-for-tiny","title":"Effective Fusion Factor in FPN for Tiny Object Detection","date":"2020-11-04","arxiv_id":"2011.02298","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-based-analysis-of-the-cultural","title":"Content-based Analysis of the Cultural Differences between TikTok and Douyin","date":"2020-11-03","arxiv_id":"2011.01414","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-contrastive-self-supervised","title":"A Survey on Contrastive Self-supervised Learning","date":"2020-10-31","arxiv_id":"2011.00362","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-comparison-of-end-to-end","title":"A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition","date":"2020-10-29","arxiv_id":"2010.15904","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-convolutional-neural-network-applied","title":"A Deep Convolutional Neural Network Applied to Ship Detection and Classification","date":"2020-10-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-3d-object-detection","title":"An Overview Of 3D Object Detection","date":"2020-10-29","arxiv_id":"2010.15614","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-neural-networks-for-video-object","title":"Recurrent Neural Networks for video object detection","date":"2020-10-29","arxiv_id":"2010.15740","repositories_listed":0,"syntology":null},{"url":null,"slug":"crpn-sfnet-a-high-performance-object-detector","title":"CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images","date":"2020-10-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-sieving-and-morphological-closing-to","title":"Object sieving and morphological closing to reduce false detections in wide-area aerial imagery","date":"2020-10-28","arxiv_id":"2010.15260","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-classification-accuracy-when-1","title":"Predicting Classification Accuracy When Adding New Unobserved Classes","date":"2020-10-28","arxiv_id":"2010.15011","repositories_listed":0,"syntology":null},{"url":null,"slug":"stereo-frustums-a-siamese-pipeline-for-3d","title":"Stereo Frustums: A Siamese Pipeline for 3D Object Detection","date":"2020-10-27","arxiv_id":"2010.14599","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-adversarial-patch-for-evading-object","title":"Dynamic Adversarial Patch for Evading Object Detection Models","date":"2020-10-25","arxiv_id":"2010.13070","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-aware-feature-aggregation-for-video","title":"Object-aware Feature Aggregation for Video Object Detection","date":"2020-10-23","arxiv_id":"2010.12573","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandwidth-adaptive-feature-sharing-for","title":"Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection","date":"2020-10-22","arxiv_id":"2010.11353","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-scale-permuted-backbone-with-1","title":"Efficient Scale-Permuted Backbone with Learned Resource Distribution","date":"2020-10-22","arxiv_id":"2010.11426","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuned-pre-trained-mask-r-cnn-models-for","title":"Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection","date":"2020-10-22","arxiv_id":"2010.11464","repositories_listed":0,"syntology":null},{"url":null,"slug":"ufo-2-a-unified-framework-towards-omni","title":"UFO$^2$: A Unified Framework towards Omni-supervised Object Detection","date":"2020-10-21","arxiv_id":"2010.10804","repositories_listed":0,"syntology":null},{"url":null,"slug":"autobss-an-efficient-algorithm-for-block","title":"AutoBSS: An Efficient Algorithm for Block Stacking Style Search","date":"2020-10-20","arxiv_id":"2010.10261","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-video-salient-object-detection-via","title":"Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation","date":"2020-10-20","arxiv_id":"2010.10027","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-with-visual-object","title":"Image Captioning with Visual Object Representations Grounded in the Textual Modality","date":"2020-10-19","arxiv_id":"2010.09413","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-super-resolution-for-dense","title":"Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation","date":"2020-10-19","arxiv_id":"2010.09594","repositories_listed":0,"syntology":null},{"url":null,"slug":"swipenet-object-detection-in-noisy-underwater","title":"SWIPENET: Object detection in noisy underwater images","date":"2020-10-19","arxiv_id":"2010.10006","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-spatio","title":"Unsupervised Domain Adaptation for Spatio-Temporal Action Localization","date":"2020-10-19","arxiv_id":"2010.09211","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-based-layout-detection-from-scientific","title":"Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks","date":"2020-10-18","arxiv_id":"2010.11727","repositories_listed":0,"syntology":null},{"url":null,"slug":"lid-2020-the-learning-from-imperfect-data","title":"LID 2020: The Learning from Imperfect Data Challenge Results","date":"2020-10-17","arxiv_id":"2010.11724","repositories_listed":0,"syntology":null},{"url":"/paper/polardet-a-fast-more-precise-detector-for","slug":"polardet-a-fast-more-precise-detector-for","title":"PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images","date":"2020-10-17","arxiv_id":"2010.08720","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpattack-diffused-patch-attacks-against","title":"DPAttack: Diffused Patch Attacks against Universal Object Detection","date":"2020-10-16","arxiv_id":"2010.11679","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-very-compact-embedded-cnn-processor-design","title":"A Very Compact Embedded CNN Processor Design Based on Logarithmic Computing","date":"2020-10-13","arxiv_id":"2010.11686","repositories_listed":0,"syntology":null},{"url":null,"slug":"satellite-image-classification-with-deep","title":"Satellite Image Classification with Deep Learning","date":"2020-10-13","arxiv_id":"2010.06497","repositories_listed":0,"syntology":null},{"url":null,"slug":"tga-two-level-group-attention-for-assembly","title":"TGA: Two-level Group Attention for Assembly State Detection","date":"2020-10-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-detect-specular-highlights-from","slug":"learning-to-detect-specular-highlights-from","title":"Learning to Detect Specular Highlights from Real-world Images","date":"2020-10-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-detection-and-pose-estimation-of","title":"3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings","date":"2020-10-08","arxiv_id":"2010.04075","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-mnasnet-reduced-mnasnet-for-computer-vision","title":"R-MnasNet: Reduced MnasNet for Computer Vision","date":"2020-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uesegnet-context-aware-unconstrained-roi","title":"UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric","date":"2020-10-08","arxiv_id":"2010.03990","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-diabetic-foot-ulcers","title":"Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation","date":"2020-10-07","arxiv_id":"2010.03341","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-monocular-3d-vehicle-detection","title":"Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels","date":"2020-10-07","arxiv_id":"2010.03506","repositories_listed":0,"syntology":null},{"url":null,"slug":"yodar-uncertainty-based-sensor-fusion-for","title":"YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors","date":"2020-10-07","arxiv_id":"2010.03320","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-coco-and-mapillary-workshop-at-iccv-1","title":"Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track","date":"2020-10-06","arxiv_id":"2010.02475","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-anomaly-detection-using-pre-trained","title":"Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining","date":"2020-10-06","arxiv_id":"2010.02406","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosted-semantic-embedding-based","title":"Enhancing Haptic Distinguishability of Surface Materials with Boosting Technique","date":"2020-10-05","arxiv_id":"2010.02002","repositories_listed":0,"syntology":null},{"url":null,"slug":"co2-consistent-contrast-for-unsupervised-1","title":"CO2: Consistent Contrast for Unsupervised Visual Representation Learning","date":"2020-10-05","arxiv_id":"2010.02217","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-generative-imagination-in-object","title":"Improving Generative Imagination in Object-Centric World Models","date":"2020-10-05","arxiv_id":"2010.02054","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-parallel-down-up-fusion-network-for-salient","title":"A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images","date":"2020-10-02","arxiv_id":"2010.00793","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-enabled-traffic","title":"Artificial Intelligence Enabled Traffic Monitoring System","date":"2020-10-02","arxiv_id":"2010.01217","repositories_listed":0,"syntology":null},{"url":null,"slug":"background-adaptive-faster-r-cnn-for-semi","title":"Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images","date":"2020-10-02","arxiv_id":"2010.01202","repositories_listed":0,"syntology":null},{"url":null,"slug":"caption-correction-by-analyses-pos-tagging","title":"CAPTION: Correction by Analyses, POS-Tagging and Interpretation of Objects using only Nouns","date":"2020-10-02","arxiv_id":"2010.00839","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-graph-learning-instance-aware","title":"Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks","date":"2020-10-02","arxiv_id":"2010.01097","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-systems-and-computer-vision","title":"Embedded Systems and Computer Vision Techniques utilized in Spray Painting Robots: A Review","date":"2020-10-02","arxiv_id":"2010.01131","repositories_listed":0,"syntology":null},{"url":"/paper/towards-automatic-visual-inspection-a-weakly","slug":"towards-automatic-visual-inspection-a-weakly","title":"Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-object-detection-from-captions-via","title":"Learning Object Detection from Captions via Textual Scene Attributes","date":"2020-09-30","arxiv_id":"2009.14558","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-differentiable-rendering-for-self-1","title":"Monocular Differentiable Rendering for Self-Supervised 3D Object Detection","date":"2020-09-30","arxiv_id":"2009.14524","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-implementation-of-rmnv2-classifier","title":"Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060","date":"2020-09-30","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-detection-of-objects-under-periodic","title":"Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering","date":"2020-09-29","arxiv_id":"2009.14178","repositories_listed":0,"syntology":null},{"url":null,"slug":"action-concept-grounding-network-for","title":"Action Concept Grounding Network for Semantically-Consistent Video Generation","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-automotive-radar-data-acquisition","title":"Adaptive Automotive Radar data Acquisition","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-detect-objects-with-a-1-megapixel","slug":"learning-to-detect-objects-with-a-1-megapixel","title":"Learning to Detect Objects with a 1 Megapixel Event Camera","date":"2020-09-28","arxiv_id":"2009.13436","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-object-detection-with-self-adaptive","title":"Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images","date":"2020-09-26","arxiv_id":"2009.12596","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-original-framework-for-wheat-head","title":"An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset","date":"2020-09-24","arxiv_id":"2009.11977","repositories_listed":0,"syntology":null},{"url":null,"slug":"coff-cooperative-spatial-feature-fusion-for","title":"CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles","date":"2020-09-24","arxiv_id":"2009.11975","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-sorghum-head-counting-when-you","title":"Image-Based Sorghum Head Counting When You Only Look Once","date":"2020-09-24","arxiv_id":"2009.11929","repositories_listed":0,"syntology":null},{"url":null,"slug":"mimicdet-bridging-the-gap-between-one-stage-1","title":"MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection","date":"2020-09-24","arxiv_id":"2009.11528","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-frame-to-single-frame-knowledge","title":"Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection","date":"2020-09-24","arxiv_id":"2009.11859","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-cross-level-attention-and-supervision","title":"CLASS: Cross-Level Attention and Supervision for Salient Objects Detection","date":"2020-09-23","arxiv_id":"2009.10916","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-learning-with-diversity-for","title":"Curriculum Learning with Diversity for Supervised Computer Vision Tasks","date":"2020-09-22","arxiv_id":"2009.10625","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-point-cloud-semantic-segmentation","title":"Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection","date":"2020-09-22","arxiv_id":"2009.10569","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-flow-in-network-feature-flow","title":"Feature Flow: In-network Feature Flow Estimation for Video Object Detection","date":"2020-09-21","arxiv_id":"2009.09660","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-s-raining-cats-or-dogs-adversarial-rain","title":"Adversarial Rain Attack and Defensive Deraining for DNN Perception","date":"2020-09-19","arxiv_id":"2009.09205","repositories_listed":0,"syntology":null},{"url":null,"slug":"moving-object-detection-for-visual-odometry","title":"Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation","date":"2020-09-18","arxiv_id":"2009.08746","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-edge-weights-in-graph-neural-networks","title":"Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection","date":"2020-09-17","arxiv_id":"2009.08253","repositories_listed":0,"syntology":null},{"url":null,"slug":"pomp-pomcp-based-online-motion-planning-for","title":"POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments","date":"2020-09-17","arxiv_id":"2009.08140","repositories_listed":0,"syntology":null},{"url":null,"slug":"radar-camera-sensor-fusion-for-joint-object","title":"Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles","date":"2020-09-17","arxiv_id":"2009.08428","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-semantic-fusion-network-for-video-object","title":"Dual Semantic Fusion Network for Video Object Detection","date":"2020-09-16","arxiv_id":"2009.07498","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-guided-learning-towards-open-domain","title":"Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision","date":"2020-09-16","arxiv_id":"2009.07470","repositories_listed":0,"syntology":null},{"url":null,"slug":"amrnet-chips-augmentation-in-areial-images","title":"AMRNet: Chips Augmentation in Aerial Images Object Detection","date":"2020-09-15","arxiv_id":"2009.07168","repositories_listed":0,"syntology":null},{"url":null,"slug":"csi2image-image-reconstruction-from-channel","title":"CSI2Image: Image Reconstruction from Channel State Information Using Generative Adversarial Networks","date":"2020-09-15","arxiv_id":"2009.07100","repositories_listed":0,"syntology":null}],"record_sha256":"97a9e35758a494a37b2673a44983e8503118d785dc0c6e764b210fe2dae95f44","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}