{"total":75,"rows":[{"id":"2203.13450","title":"A Comparative Survey of Deep Active Learning","created":"2022-03-25","cited_by_count":89,"area":null},{"id":"2204.07965","title":"Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint","created":"2022-04-17","cited_by_count":53,"area":null},{"id":"2405.15613","title":"Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach","created":"2024-05-24","cited_by_count":50,"area":null},{"id":"2304.00733","title":"Unbiased Scene Graph Generation in Videos","created":"2023-04-03","cited_by_count":39,"area":null},{"id":"2310.14230","title":"A comprehensive survey on deep active learning in medical image analysis","created":"2023-10-22","cited_by_count":35,"area":null},{"id":"2211.11612","title":"Plug and Play Active Learning for Object Detection","created":"2022-11-21","cited_by_count":28,"area":null},{"id":"2106.11921","title":"Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection","created":"2021-06-22","cited_by_count":27,"area":null},{"id":"2301.09249","title":"Exploring Active 3D Object Detection from a Generalization Perspective","created":"2023-01-23","cited_by_count":26,"area":null},{"id":"2303.02535","title":"Streaming Active Learning with Deep Neural Networks","created":"2023-03-05","cited_by_count":20,"area":null},{"id":"2210.08375","title":"Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining","created":"2022-10-15","cited_by_count":19,"area":null},{"id":"2307.00934","title":"Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration","created":"2023-07-03","cited_by_count":19,"area":null},{"id":"2207.12112","title":"Active Learning Strategies for Weakly-supervised Object Detection","created":"2022-07-25","cited_by_count":17,"area":null},{"id":"2203.10730","title":"Semantic Segmentation with Active Semi-Supervised Learning","created":"2022-03-21","cited_by_count":16,"area":null},{"id":"2207.11493","title":"Active Pointly-Supervised Instance Segmentation","created":"2022-07-23","cited_by_count":15,"area":null},{"id":"2203.05053","title":"Optical Flow Training under Limited Label Budget via Active Learning","created":"2022-03-09","cited_by_count":14,"area":null},{"id":"2303.13089","title":"Box-Level Active Detection","created":"2023-03-23","cited_by_count":13,"area":null},{"id":"2307.07942","title":"KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection","created":"2023-07-16","cited_by_count":13,"area":null},{"id":"2307.08415","title":"Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning","created":"2023-07-17","cited_by_count":10,"area":null},{"id":"2309.17342","title":"Towards Free Data Selection with General-Purpose Models","created":"2023-09-29","cited_by_count":10,"area":null},{"id":"2312.10469","title":"One step closer to unbiased aleatoric uncertainty estimation","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2112.05298","title":"IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes","created":"2021-12-10","cited_by_count":9,"area":null},{"id":"2306.12398","title":"Multi-Task Consistency for Active Learning","created":"2023-06-21","cited_by_count":9,"area":null},{"id":"2109.11547","title":"Bayesian Active Learning for Sim-to-Real Robotic Perception","created":"2021-09-23","cited_by_count":8,"area":null},{"id":"2309.07914","title":"ALWOD: Active Learning for Weakly-Supervised Object Detection","created":"2023-09-14","cited_by_count":8,"area":null},{"id":"2207.13339","title":"ALBench: A Framework for Evaluating Active Learning in Object Detection","created":"2022-07-27","cited_by_count":7,"area":null},{"id":"2211.14819","title":"Deep Active Learning for Computer Vision: Past and Future","created":"2022-11-27","cited_by_count":7,"area":null},{"id":"2409.13860","title":"SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation","created":"2024-09-20","cited_by_count":7,"area":null},{"id":"2112.07963","title":"Towards General and Efficient Active Learning","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2509.20745","title":"Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection","created":"2025-09-25","cited_by_count":6,"area":null},{"id":"2410.23910","title":"Uncertainty Estimation for 3D Object Detection via Evidential Learning","created":"2024-10-31","cited_by_count":5,"area":null},{"id":"2307.08414","title":"Active Learning for Object Detection with Non-Redundant Informative Sampling","created":"2023-07-17","cited_by_count":4,"area":null},{"id":"2410.02268","title":"Structural-Entropy-Based Sample Selection for Efficient and Effective Learning","created":"2024-10-03","cited_by_count":4,"area":null},{"id":"2208.07497","title":"Bucketized Active Sampling for Learning ACOPF","created":"2022-08-16","cited_by_count":3,"area":null},{"id":"2212.10836","title":"Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection","created":"2022-12-21","cited_by_count":3,"area":null},{"id":"2302.10396","title":"Assessing Domain Gap for Continual Domain Adaptation in Object Detection","created":"2023-02-21","cited_by_count":3,"area":null},{"id":"2310.00372","title":"Deep Active Learning with Noisy Oracle in Object Detection","created":"2023-09-30","cited_by_count":3,"area":null},{"id":"2404.17427","title":"Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection","created":"2024-04-26","cited_by_count":3,"area":null},{"id":"2508.13983","title":"OmViD: Omni-supervised active learning for video action detection","created":"2025-08-19","cited_by_count":3,"area":null},{"id":"2508.19906","title":"Streamlining the Development of Active Learning Methods in Real-World Object Detection","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2209.00746","title":"MIME: Minority Inclusion for Majority Group Enhancement of AI Performance","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2209.11559","title":"Query-based Hard-Image Retrieval for Object Detection at Test Time","created":"2022-09-23","cited_by_count":2,"area":null},{"id":"2212.06493","title":"Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection","created":"2022-12-13","cited_by_count":2,"area":null},{"id":"2312.16931","title":"DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query","created":"2023-12-28","cited_by_count":2,"area":null},{"id":"2403.02566","title":"Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning","created":"2024-03-05","cited_by_count":2,"area":null},{"id":"2605.10349","title":"Portable Active Learning for Object Detection","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2210.09678","title":"Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities","created":"2022-10-18","cited_by_count":1,"area":null},{"id":"2212.04211","title":"Evaluating Zero-cost Active Learning for Object Detection","created":"2022-12-08","cited_by_count":1,"area":null},{"id":"2308.08476","title":"Classification Committee for Active Deep Object Detection","created":"2023-08-16","cited_by_count":1,"area":null},{"id":"2309.16139","title":"Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling","created":"2023-09-28","cited_by_count":1,"area":null},{"id":"2403.14800","title":"Deep Active Learning: A Reality Check","created":"2024-03-21","cited_by_count":1,"area":null},{"id":"2407.01731","title":"Uncertainty Quantification in Table Structure Recognition","created":"2024-07-01","cited_by_count":1,"area":null},{"id":"2409.05425","title":"Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2410.03918","title":"STONE: A Submodular Optimization Framework for Active 3D Object Detection","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2505.00511","title":"Inconsistency-based Active Learning for LiDAR Object Detection","created":"2025-05-01","cited_by_count":1,"area":null},{"id":"2509.20003","title":"Table Detection with Active Learning","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2110.08265","title":"Knowledge-driven Active Learning","created":"2021-10-15","cited_by_count":0,"area":null},{"id":"2207.08782","title":"Instance-Aware Observer Network for Out-of-Distribution Object Segmentation","created":"2022-07-18","cited_by_count":0,"area":null},{"id":"2303.12760","title":"Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains","created":"2023-03-22","cited_by_count":0,"area":null},{"id":"2303.16637","title":"MuRAL: Multi-Scale Region-based Active Learning for Object Detection","created":"2023-03-29","cited_by_count":0,"area":null},{"id":"2310.10391","title":"Open-CRB: Towards Open World Active Learning for 3D Object Detection","created":"2023-10-16","cited_by_count":0,"area":null},{"id":"2407.15797","title":"MILAN: Milli-Annotations for Lidar Semantic Segmentation","created":"2024-07-22","cited_by_count":0,"area":null},{"id":"2408.17098","title":"UTrack: Multi-Object Tracking with Uncertain Detections","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2409.03543","title":"Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift","created":"2024-09-05","cited_by_count":0,"area":null},{"id":"2412.18870","title":"TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection","created":"2024-12-25","cited_by_count":0,"area":null},{"id":"2501.15449","title":"Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection","created":"2025-01-26","cited_by_count":0,"area":null},{"id":"2505.00507","title":"HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection","created":"2025-05-01","cited_by_count":0,"area":null},{"id":"2511.22567","title":"Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2604.17298","title":"Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.07549","title":"Probabilistic Object Detection with Conformal Prediction","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.12303","title":"From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.22169","title":"Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2608.30621","title":"Cost-efficient Active Learning for Referring Image Segmentation and Grounding","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.02212","title":"FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.20248","title":"Distance to Class Prototypes: Active Learning for Object Detection","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2609.35449","title":"From internal representations to model improvement through prediction errors","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}