{"total":21,"rows":[{"id":"2405.14793","title":"SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow","created":"2024-05-23","cited_by_count":174,"area":null},{"id":"2101.01710","title":"Learning Accurate Dense Correspondences and When to Trust Them","created":"2021-01-05","cited_by_count":113,"area":null},{"id":"2109.13912","title":"PDC-Net+: Enhanced Probabilistic Dense Correspondence Network","created":"2021-09-28","cited_by_count":80,"area":null},{"id":"2305.12998","title":"MFT: Long-Term Tracking of Every Pixel","created":"2023-05-22","cited_by_count":62,"area":null},{"id":"2307.15615","title":"A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond","created":"2023-07-28","cited_by_count":55,"area":null},{"id":"2203.04279","title":"Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences","created":"2022-03-08","cited_by_count":30,"area":null},{"id":"2208.02005","title":"Gradient-based Uncertainty for Monocular Depth Estimation","created":"2022-08-03","cited_by_count":26,"area":null},{"id":"2409.09479","title":"MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry","created":"2024-09-14","cited_by_count":19,"area":null},{"id":"2302.08149","title":"URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation","created":"2023-02-16","cited_by_count":16,"area":null},{"id":"2405.04278","title":"Uncertainty Quantification Metrics for Deep Regression","created":"2024-05-07","cited_by_count":16,"area":null},{"id":"2210.03118","title":"Unsupervised confidence for LiDAR depth maps and applications","created":"2022-10-06","cited_by_count":10,"area":null},{"id":"2507.08400","title":"PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models","created":"2025-07-11","cited_by_count":2,"area":null},{"id":"2210.06811","title":"On the calibration of underrepresented classes in LiDAR-based semantic segmentation","created":"2022-10-13","cited_by_count":1,"area":null},{"id":"2212.04831","title":"Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models","created":"2022-12-09","cited_by_count":1,"area":null},{"id":"2307.09929","title":"Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation","created":"2023-07-19","cited_by_count":1,"area":null},{"id":"2207.03182","title":"Chilled Sampling for Uncertainty Quantification: A Motivation From A Meteorological Inverse Problem","created":"2022-07-07","cited_by_count":0,"area":null},{"id":"2305.08744","title":"Integrating Uncertainty into Neural Network-based Speech Enhancement","created":"2023-05-15","cited_by_count":0,"area":null},{"id":"2305.19780","title":"A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles","created":"2023-05-31","cited_by_count":0,"area":null},{"id":"2510.06754","title":"UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2604.10056","title":"U$^{2}$Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation","created":"2026-04-11","cited_by_count":0,"area":null},{"id":"2609.15171","title":"EECTracker: Swarm Motion Prior-Guided Feature Compensation for Airborne Optical UAV Swarm Tracking","created":"2026-09-14","cited_by_count":0,"area":null}],"next_cursor":null}