{"total":47,"rows":[{"id":"2104.14235","title":"Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety","created":"2021-04-29","cited_by_count":35,"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":"2104.06394","title":"All you need are a few pixels: semantic segmentation with PixelPick","created":"2021-04-13","cited_by_count":31,"area":null},{"id":"2108.12545","title":"Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation","created":"2021-08-28","cited_by_count":25,"area":null},{"id":"2401.08501","title":"ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation","created":"2024-01-16","cited_by_count":25,"area":null},{"id":"2210.14142","title":"From colouring-in to pointillism: revisiting semantic segmentation supervision","created":"2022-10-25","cited_by_count":22,"area":null},{"id":"2101.06931","title":"Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach","created":"2021-01-18","cited_by_count":17,"area":null},{"id":"2202.12588","title":"Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning","created":"2022-02-25","cited_by_count":16,"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":"2211.05997","title":"LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation","created":"2022-11-11","cited_by_count":16,"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":"2205.13340","title":"Deep Active Learning with Noise Stability","created":"2022-05-26","cited_by_count":14,"area":null},{"id":"2303.16817","title":"Adaptive Superpixel for Active Learning in Semantic Segmentation","created":"2023-03-29","cited_by_count":14,"area":null},{"id":"2302.04075","title":"Best Practices in Active Learning for Semantic Segmentation","created":"2023-02-08","cited_by_count":10,"area":null},{"id":"2110.07782","title":"Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images","created":"2021-10-15","cited_by_count":9,"area":null},{"id":"2309.09319","title":"Active Learning for Semantic Segmentation with Multi-class Label Query","created":"2023-09-17","cited_by_count":9,"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":"2403.10820","title":"Active Label Correction for Semantic Segmentation with Foundation Models","created":"2024-03-16","cited_by_count":7,"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":"2106.11858","title":"MEAL: Manifold Embedding-based Active Learning","created":"2021-06-22","cited_by_count":5,"area":null},{"id":"2202.06484","title":"D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation","created":"2022-02-14","cited_by_count":5,"area":null},{"id":"2408.01694","title":"Bayesian Active Learning for Semantic Segmentation","created":"2024-08-03","cited_by_count":5,"area":null},{"id":"2311.08811","title":"Correlation-aware active learning for surgery video segmentation","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2405.18078","title":"Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2112.13709","title":"Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training","created":"2021-12-27","cited_by_count":3,"area":null},{"id":"2307.07168","title":"Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation","created":"2023-07-14","cited_by_count":3,"area":null},{"id":"2307.10745","title":"EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation","created":"2023-07-20","cited_by_count":3,"area":null},{"id":"2104.03956","title":"Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes","created":"2021-04-08","cited_by_count":2,"area":null},{"id":"2202.09002","title":"An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation","created":"2022-02-18","cited_by_count":2,"area":null},{"id":"2206.00181","title":"Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection","created":"2022-06-01","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":"2412.06470","title":"Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation","created":"2024-12-09","cited_by_count":2,"area":null},{"id":"2203.06215","title":"Can I see an Example? Active Learning the Long Tail of Attributes and Relations","created":"2022-03-11","cited_by_count":1,"area":null},{"id":"2407.06363","title":"Leveraging image captions for selective whole slide image annotation","created":"2024-07-08","cited_by_count":1,"area":null},{"id":"2507.17367","title":"Exploring Spatial Diversity for Region-based Active Learning","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2512.07606","title":"Decomposition Sampling for Efficient Region Annotations in Active Learning","created":"2025-12-08","cited_by_count":1,"area":null},{"id":"2112.14084","title":"Embodied Learning for Lifelong Visual Perception","created":"2021-12-28","cited_by_count":0,"area":null},{"id":"2205.14917","title":"Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning","created":"2022-05-30","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":"2403.15143","title":"Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project","created":"2024-03-22","cited_by_count":0,"area":null},{"id":"2603.11498","title":"ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2604.20000","title":"RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2606.10136","title":"iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision","created":"2026-06-08","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":"2610.17808","title":"SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis","created":"2026-10-13","cited_by_count":0,"area":null},{"id":"2610.20396","title":"From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}