{"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/51","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":51,"pages_in_order":110,"rows_per_page":100,"rows":[5001,5100],"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/50","next":"/task/object-detection/papers/52","papers":[{"url":null,"slug":"an-object-detection-approach-for-lane-change","title":"An object detection approach for lane change and overtake detection from motion profiles","date":"2025-02-06","arxiv_id":"2502.04244","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimized-yolov5-based-approach-for-real","title":"An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras","date":"2025-02-06","arxiv_id":"2502.04566","repositories_listed":0,"syntology":null},{"url":null,"slug":"onetrack-m-a-multitask-approach-to","title":"OneTrack-M: A multitask approach to transformer-based MOT models","date":"2025-02-06","arxiv_id":"2502.04478","repositories_listed":0,"syntology":null},{"url":null,"slug":"pursuing-better-decision-boundaries-for-long","title":"Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount","date":"2025-02-06","arxiv_id":"2502.03852","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-domain-generalized-object-detection-by","title":"Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance","date":"2025-02-06","arxiv_id":"2502.03835","repositories_listed":0,"syntology":null},{"url":null,"slug":"uav-cognitive-semantic-communications-enabled","title":"UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection","date":"2025-02-06","arxiv_id":"2502.03761","repositories_listed":0,"syntology":null},{"url":null,"slug":"yolov4-a-breakthrough-in-real-time-object","title":"YOLOv4: A Breakthrough in Real-Time Object Detection","date":"2025-02-06","arxiv_id":"2502.04161","repositories_listed":0,"syntology":null},{"url":null,"slug":"robograsp-a-universal-grasping-policy-for","title":"RoboGrasp: A Universal Grasping Policy for Robust Robotic Control","date":"2025-02-05","arxiv_id":"2502.03072","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-spatial-language-grounding-through","title":"Exploring Spatial Language Grounding Through Referring Expressions","date":"2025-02-04","arxiv_id":"2502.04359","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-fog-to-failure-how-dehazing-can-harm","title":"From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection","date":"2025-02-04","arxiv_id":"2502.02027","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-ropes-better-2d-and-3d-position","title":"Learning the RoPEs: Better 2D and 3D Position Encodings with STRING","date":"2025-02-04","arxiv_id":"2502.02562","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-transformer-adapter-for","title":"Memory Efficient Transformer Adapter for Dense Predictions","date":"2025-02-04","arxiv_id":"2502.01962","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-collaborative","title":"Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks","date":"2025-02-04","arxiv_id":"2502.02537","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaucho-gaussian-distributions-with-cholesky","title":"GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection","date":"2025-02-03","arxiv_id":"2502.01565","repositories_listed":0,"syntology":null},{"url":null,"slug":"khait-k-9-handler-artificial-intelligence","title":"KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking","date":"2025-02-03","arxiv_id":"2503.15524","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigation-of-camouflaged-adversarial-attacks","title":"Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulator","date":"2025-02-03","arxiv_id":"2502.05208","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliability-driven-lidar-camera-fusion-for","title":"Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection","date":"2025-02-03","arxiv_id":"2502.01856","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-diagnosis-and-severity-assessment-of","title":"Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques","date":"2025-01-31","arxiv_id":"2501.18835","repositories_listed":0,"syntology":null},{"url":null,"slug":"let-human-sketches-help-empowering","title":"Let Human Sketches Help: Empowering Challenging Image Segmentation Task with Freehand Sketches","date":"2025-01-31","arxiv_id":"2501.19329","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-object-detection-for-indoor","title":"Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms","date":"2025-01-30","arxiv_id":"2501.18444","repositories_listed":0,"syntology":null},{"url":null,"slug":"iroam-improving-roadside-monocular-3d-object","title":"IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain","date":"2025-01-30","arxiv_id":"2501.18162","repositories_listed":0,"syntology":null},{"url":null,"slug":"tuning-event-camera-biases-heuristic-for","title":"Tuning Event Camera Biases Heuristic for Object Detection Applications in Staring Scenarios","date":"2025-01-30","arxiv_id":"2501.18788","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-scheduling-framework-for-multi","title":"Real Time Scheduling Framework for Multi Object Detection via Spiking Neural Networks","date":"2025-01-29","arxiv_id":"2501.18412","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-self-paced-learning-for-weakly","title":"Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding","date":"2025-01-28","arxiv_id":"2501.17053","repositories_listed":0,"syntology":null},{"url":null,"slug":"debugagent-efficient-and-interpretable-error","title":"DebugAgent: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging","date":"2025-01-28","arxiv_id":"2501.16751","repositories_listed":0,"syntology":null},{"url":null,"slug":"dinostar-deep-iterative-neural-object","title":"DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications","date":"2025-01-28","arxiv_id":"2501.17076","repositories_listed":0,"syntology":null},{"url":null,"slug":"modulating-cnn-features-with-pre-trained-vit","title":"Modulating CNN Features with Pre-Trained ViT Representations for Open-Vocabulary Object Detection","date":"2025-01-28","arxiv_id":"2501.16981","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssf-pan-semantic-scene-flow-based-perception","title":"SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios","date":"2025-01-28","arxiv_id":"2501.16754","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-object-detection-of-marine-debris","title":"Efficient Object Detection of Marine Debris using Pruned YOLO Model","date":"2025-01-27","arxiv_id":"2501.16571","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-for-medical-image-analysis","title":"Object Detection for Medical Image Analysis: Insights from the RT-DETR Model","date":"2025-01-27","arxiv_id":"2501.16469","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-linear-attention-resurrection-in-vision","title":"The Linear Attention Resurrection in Vision Transformer","date":"2025-01-27","arxiv_id":"2501.16182","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-ssl-al-barrier-a-synergistic","title":"Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection","date":"2025-01-26","arxiv_id":"2501.15449","repositories_listed":0,"syntology":null},{"url":"/paper/cisol-an-open-and-extensible-dataset-for","slug":"cisol-an-open-and-extensible-dataset-for","title":"CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry","date":"2025-01-26","arxiv_id":"2501.15469","repositories_listed":0,"syntology":null},{"url":null,"slug":"doracamom-joint-3d-detection-and-occupancy","title":"Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception","date":"2025-01-26","arxiv_id":"2501.15394","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-yolo-based-dyslexia-detection-in","title":"Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data","date":"2025-01-25","arxiv_id":"2501.15263","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-aided-channel-prediction-for-vehicular","title":"Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images","date":"2025-01-25","arxiv_id":"2501.18618","repositories_listed":0,"syntology":null},{"url":null,"slug":"td-rd-a-top-down-benchmark-with-real-time","title":"TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection","date":"2025-01-24","arxiv_id":"2501.14302","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-precision-control-in-object","title":"Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting","date":"2025-01-23","arxiv_id":"2501.14036","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-pec-yolo-for-detecting-improper","title":"Enhanced PEC-YOLO for Detecting Improper Safety Gear Wearing Among Power Line Workers","date":"2025-01-23","arxiv_id":"2501.13981","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-lessons-learned-of-an-artificial","title":"First Lessons Learned of an Artificial Intelligence Robotic System for Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods","date":"2025-01-23","arxiv_id":"2501.13855","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-causality-with-neurochaos","title":"Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda","date":"2025-01-23","arxiv_id":"2501.13763","repositories_listed":0,"syntology":null},{"url":null,"slug":"mona-moving-object-detection-from-videos-shot","title":"MONA: Moving Object Detection from Videos Shot by Dynamic Camera","date":"2025-01-22","arxiv_id":"2501.13183","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-paced-learning-strategy-based-on","title":"Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training","date":"2025-01-21","arxiv_id":"2501.12071","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlen-dual-branch-of-transformer-for-low-light","title":"DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains","date":"2025-01-21","arxiv_id":"2501.12235","repositories_listed":0,"syntology":null},{"url":null,"slug":"svgs-dsgat-an-iot-enabled-innovation-in","title":"SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology","date":"2025-01-21","arxiv_id":"2501.12169","repositories_listed":0,"syntology":null},{"url":null,"slug":"toffe-temporally-binned-object-flow-from","title":"TOFFE -- Temporally-binned Object Flow from Events for High-speed and Energy-Efficient Object Detection and Tracking","date":"2025-01-21","arxiv_id":"2501.12482","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sar-object-detection-with-self","title":"Enhancing SAR Object Detection with Self-Supervised Pre-training on Masked Auto-Encoders","date":"2025-01-20","arxiv_id":"2501.11249","repositories_listed":0,"syntology":null},{"url":null,"slug":"green-video-camouflaged-object-detection","title":"Green Video Camouflaged Object Detection","date":"2025-01-19","arxiv_id":"2501.10914","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-confident-image-regions-for-source","title":"Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection","date":"2025-01-17","arxiv_id":"2501.10081","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutualforce-mutual-aware-enhancement-for-4d","title":"MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection","date":"2025-01-17","arxiv_id":"2501.10266","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-d-piece-image-tokenizer-meets-quality","title":"One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression","date":"2025-01-17","arxiv_id":"2501.10064","repositories_listed":0,"syntology":null},{"url":null,"slug":"monosowa-scalable-monocular-3d-object","title":"MonoSOWA: Scalable monocular 3D Object detector Without human Annotations","date":"2025-01-16","arxiv_id":"2501.09481","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-relation-between-optical-aperture-and","title":"On the Relation between Optical Aperture and Automotive Object Detection","date":"2025-01-16","arxiv_id":"2501.09456","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-pose-synergizing-reinforcement-learning","title":"RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection","date":"2025-01-16","arxiv_id":"2501.09465","repositories_listed":0,"syntology":null},{"url":null,"slug":"soccersynth-detection-a-synthetic-dataset-for","title":"SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection","date":"2025-01-16","arxiv_id":"2501.09281","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-devil-is-in-the-details-simple-remedies","title":"The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning","date":"2025-01-16","arxiv_id":"2501.09485","repositories_listed":0,"syntology":null},{"url":null,"slug":"pacf-prototype-augmented-compact-features-for","title":"PACF: Prototype Augmented Compact Features for Improving Domain Adaptive Object Detection","date":"2025-01-15","arxiv_id":"2501.08605","repositories_listed":0,"syntology":null},{"url":null,"slug":"polyp-detection-in-colonoscopy-images-using","title":"Polyp detection in colonoscopy images using YOLOv11","date":"2025-01-15","arxiv_id":"2501.09051","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-corner-cases-high-resolution","title":"Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying","date":"2025-01-14","arxiv_id":"2501.08142","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-scale-aware-adaptive-masked-knowledge","title":"Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection","date":"2025-01-13","arxiv_id":"2501.07101","repositories_listed":0,"syntology":null},{"url":null,"slug":"ml-mule-mobile-driven-context-aware","title":"ML Mule: Mobile-Driven Context-Aware Collaborative Learning","date":"2025-01-13","arxiv_id":"2501.07536","repositories_listed":0,"syntology":null},{"url":"/paper/cpdr-towards-highly-efficient-salient-object","slug":"cpdr-towards-highly-efficient-salient-object","title":"CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement","date":"2025-01-11","arxiv_id":"2501.06441","repositories_listed":0,"syntology":null},{"url":null,"slug":"focusdd-real-world-scene-infusion-for-robust","title":"FocusDD: Real-World Scene Infusion for Robust Dataset Distillation","date":"2025-01-11","arxiv_id":"2501.06405","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-holistically-point-guided-text-framework","title":"A Holistically Point-guided Text Framework for Weakly-Supervised Camouflaged Object Detection","date":"2025-01-10","arxiv_id":"2501.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimizing-occlusion-effect-on-multi-view","title":"Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion","date":"2025-01-10","arxiv_id":"2501.05997","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-supervised-object-distance","title":"Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles","date":"2025-01-09","arxiv_id":"2501.05567","repositories_listed":0,"syntology":null},{"url":null,"slug":"corrdiff-adaptive-delay-aware-detector-with","title":"CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection","date":"2025-01-09","arxiv_id":"2501.05132","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-of-yolov7-in-kitchen-safety-while","title":"Performance of YOLOv7 in Kitchen Safety While Handling Knife","date":"2025-01-09","arxiv_id":"2501.05399","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-salient-object-detection-with-1","title":"Boosting Salient Object Detection with Knowledge Distillated from Large Foundation Models","date":"2025-01-08","arxiv_id":"2501.04582","repositories_listed":0,"syntology":null},{"url":null,"slug":"fgu3r-fine-grained-fusion-via-unified-3d","title":"FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection","date":"2025-01-08","arxiv_id":"2501.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"h-mba-hierarchical-mamba-adaptation-for-multi","title":"H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving","date":"2025-01-08","arxiv_id":"2501.04302","repositories_listed":0,"syntology":null},{"url":null,"slug":"upaq-a-framework-for-real-time-and-energy","title":"UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles","date":"2025-01-08","arxiv_id":"2501.04213","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-summarisation-with-incident-and-context","title":"Video Summarisation with Incident and Context Information using Generative AI","date":"2025-01-08","arxiv_id":"2501.04764","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-triplet-net-progress-recognition","title":"Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work","date":"2025-01-07","arxiv_id":"2501.03533","repositories_listed":0,"syntology":null},{"url":null,"slug":"auxdepthnet-real-time-monocular-3d-object","title":"AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features","date":"2025-01-07","arxiv_id":"2501.03700","repositories_listed":0,"syntology":null},{"url":null,"slug":"largead-large-scale-cross-sensor-data","title":"LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving","date":"2025-01-07","arxiv_id":"2501.04005","repositories_listed":0,"syntology":null},{"url":null,"slug":"scc-yolo-an-improved-object-detector-for","title":"SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis","date":"2025-01-07","arxiv_id":"2501.03836","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-from-early","title":"Visual question answering: from early developments to recent advances -- a survey","date":"2025-01-07","arxiv_id":"2501.03939","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-crop-yield-estimation-of-blueberries","title":"Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones","date":"2025-01-04","arxiv_id":"2501.02344","repositories_listed":0,"syntology":null},{"url":null,"slug":"msc-bench-benchmarking-and-analyzing-multi","title":"MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception","date":"2025-01-02","arxiv_id":"2501.01037","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-using-capsnet-and-deep","title":"A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia","date":"2025-01-01","arxiv_id":"2501.00876","repositories_listed":0,"syntology":null},{"url":null,"slug":"abbspo-adaptive-bounding-box-scaling-and","title":"ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-collaborative-graph","title":"Asynchronous Collaborative Graph Representation for Frames and Events","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-inspired-spiking-neural-networks-for-1","title":"Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"camuvid-calibration-free-multi-view-detection","title":"CaMuViD: Calibration-Free Multi-View Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"corrbev-multi-view-3d-object-detection-by","title":"CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"defmamba-deformable-visual-state-space-model","title":"DefMamba: Deformable Visual State Space Model","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-open-world-objects-via-partial","title":"Detecting Open World Objects via Partial Attribute Assignment","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-event-based-object-detection-a","title":"Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-test-time-adaptive-object-detection","title":"Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-information-driven-position-gaussian","title":"Feature Information Driven Position Gaussian Distribution Estimation for Tiny Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fshnet-fully-sparse-hybrid-network-for-3d","title":"FSHNet: Fully Sparse Hybrid Network for 3D Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gblobs-explicit-local-structure-via-gaussian","title":"GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-map-priors-for-collaborative-bev","title":"Generative Map Priors for Collaborative BEV Semantic Segmentation","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-class-prototypes-for-unified-sparse-1","title":"Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-endogenous-attention-for-incremental","title":"Learning Endogenous Attention for Incremental Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-temporal-cues-for-semi-supervised","title":"Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-world-objectness-modeling-unifies-novel","title":"Open-World Objectness Modeling Unifies Novel Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"percept-memory-and-imagine-world-feature","title":"Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pillarhist-a-quantization-aware-pillar-1","title":"PillarHist: A Quantization-aware Pillar Feature Encoder based on Height-aware Histogram","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"d293b474c48f084510cd2f29f733b262092f1bbad408eb8f104828d42d316220","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}