{"total":62,"rows":[{"id":"2109.14279","title":"Localizing Objects with Self-Supervised Transformers and no Labels","created":"2021-09-29","cited_by_count":230,"area":null},{"id":"2202.11539","title":"Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut","created":"2022-02-23","cited_by_count":167,"area":"distill"},{"id":"2209.00383","title":"TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut","created":"2022-09-01","cited_by_count":113,"area":null},{"id":"2103.16130","title":"Active Learning for Deep Object Detection via Probabilistic Modeling","created":"2021-03-30","cited_by_count":79,"area":null},{"id":"2104.02324","title":"Multiple instance active learning for object detection","created":"2021-04-06","cited_by_count":77,"area":null},{"id":"2110.11325","title":"Learning 3D Semantic Segmentation with only 2D Image Supervision","created":"2021-10-21","cited_by_count":60,"area":null},{"id":"2402.14873","title":"Technical Report on the Pangram AI-Generated Text Classifier","created":"2024-02-21","cited_by_count":34,"area":null},{"id":"2204.02488","title":"Discovering and forecasting extreme events via active learning in neural operators","created":"2022-04-05","cited_by_count":32,"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":"2110.13259","title":"Active Learning for Deep Visual Tracking","created":"2021-10-17","cited_by_count":19,"area":null},{"id":"2107.11049","title":"MCDAL: Maximum Classifier Discrepancy for Active Learning","created":"2021-07-23","cited_by_count":18,"area":null},{"id":"2203.10730","title":"Semantic Segmentation with Active Semi-Supervised Learning","created":"2022-03-21","cited_by_count":16,"area":null},{"id":"2205.07708","title":"Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving","created":"2022-05-16","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":"2308.10174","title":"Neural Interactive Keypoint Detection","created":"2023-08-20","cited_by_count":12,"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":"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":"2112.06586","title":"Active learning with MaskAL reduces annotation effort for training Mask R-CNN","created":"2021-12-13","cited_by_count":7,"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":"2107.14707","title":"When Deep Learners Change Their Mind: Learning Dynamics for Active Learning","created":"2021-07-30","cited_by_count":6,"area":null},{"id":"2112.05975","title":"CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation","created":"2021-12-11","cited_by_count":6,"area":null},{"id":"2110.07727","title":"Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations","created":"2021-10-08","cited_by_count":5,"area":null},{"id":"2111.10046","title":"YMIR: A Rapid Data-centric Development Platform for Vision Applications","created":"2021-11-19","cited_by_count":5,"area":null},{"id":"2111.04286","title":"Deep Unsupervised Active Learning on Learnable Graphs","created":"2021-11-08","cited_by_count":4,"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":"2101.03020","title":"Dataset Definition Standard (DDS)","created":"2021-01-07","cited_by_count":3,"area":null},{"id":"2212.02804","title":"MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection","created":"2022-12-06","cited_by_count":3,"area":null},{"id":"2304.07031","title":"Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2310.09793","title":"Automated Detection of Cat Facial Landmarks","created":"2023-10-15","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":"2601.02409","title":"Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis","created":"2026-01-02","cited_by_count":3,"area":null},{"id":"2108.09186","title":"Region-level Active Detector Learning","created":"2021-08-20","cited_by_count":2,"area":null},{"id":"2202.02661","title":"LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation","created":"2022-02-06","cited_by_count":2,"area":null},{"id":"2206.01014","title":"Suggestive Annotation of Brain MR Images with Gradient-guided Sampling","created":"2022-06-02","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":"2308.00721","title":"A Pre-trained Data Deduplication Model based on Active Learning","created":"2023-07-31","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":"2403.06174","title":"Domain Adversarial Active Learning for Domain Generalization Classification","created":"2024-03-10","cited_by_count":2,"area":null},{"id":"2601.15688","title":"Performance-guided Reinforced Active Learning for Object Detection","created":"2026-01-22","cited_by_count":2,"area":"deeprl"},{"id":"2602.13308","title":"Learning to Select Like Humans: Explainable Active Learning for Medical Imaging","created":"2026-02-10","cited_by_count":2,"area":null},{"id":"2208.08220","title":"Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System","created":"2022-08-17","cited_by_count":1,"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":"2302.12667","title":"Deep active learning for nonlinear system identification","created":"2023-02-24","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":"2403.14800","title":"Deep Active Learning: A Reality Check","created":"2024-03-21","cited_by_count":1,"area":null},{"id":"2409.18561","title":"AL-GTD: Deep Active Learning for Gaze Target Detection","created":"2024-09-27","cited_by_count":1,"area":null},{"id":"2110.02069","title":"OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis","created":"2021-10-01","cited_by_count":0,"area":null},{"id":"2110.06287","title":"Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution","created":"2021-10-12","cited_by_count":0,"area":null},{"id":"2112.07356","title":"Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry","created":"2021-12-11","cited_by_count":0,"area":null},{"id":"2208.14856","title":"Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization","created":"2022-08-31","cited_by_count":0,"area":null},{"id":"2302.10679","title":"Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset","created":"2023-02-21","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":"2308.14904","title":"Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2310.08387","title":"Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection","created":"2023-10-12","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":"2405.16301","title":"Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2411.19835","title":"Feedback-driven object detection and iterative model improvement","created":"2024-11-29","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}],"next_cursor":null}