{"total":127,"rows":[{"id":"2111.13680","title":"GMFlow: Learning Optical Flow via Global Matching","created":"2021-11-26","cited_by_count":327,"area":null},{"id":"2211.05783","title":"Unifying Flow, Stereo and Depth Estimation","created":"2022-11-10","cited_by_count":275,"area":null},{"id":"2104.02699","title":"ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement","created":"2021-04-06","cited_by_count":248,"area":null},{"id":"2104.02409","title":"Learning to Estimate Hidden Motions with Global Motion Aggregation","created":"2021-04-06","cited_by_count":224,"area":null},{"id":"2304.09790","title":"AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation","created":"2023-04-19","cited_by_count":180,"area":null},{"id":"2202.04901","title":"FILM: Frame Interpolation for Large Motion","created":"2022-02-10","cited_by_count":153,"area":null},{"id":"2108.10552","title":"E-RAFT: Dense Optical Flow from Event Cameras","created":"2021-08-24","cited_by_count":152,"area":null},{"id":"2103.16206","title":"XVFI: eXtreme Video Frame Interpolation","created":"2021-03-30","cited_by_count":126,"area":null},{"id":"2104.14544","title":"AutoFlow: Learning a Better Training Set for Optical Flow","created":"2021-04-29","cited_by_count":102,"area":null},{"id":"2203.16896","title":"CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow","created":"2022-03-31","cited_by_count":82,"area":null},{"id":"2112.08122","title":"Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue","created":"2021-12-15","cited_by_count":72,"area":null},{"id":"2203.11335","title":"Global Matching with Overlapping Attention for Optical Flow Estimation","created":"2022-03-21","cited_by_count":64,"area":null},{"id":"2108.00622","title":"Recurrent Mask Refinement for Few-Shot Medical Image Segmentation","created":"2021-08-02","cited_by_count":63,"area":null},{"id":"2305.12998","title":"MFT: Long-Term Tracking of Every Pixel","created":"2023-05-22","cited_by_count":62,"area":null},{"id":"2211.09078","title":"Learning Dense and Continuous Optical Flow from an Event Camera","created":"2022-11-16","cited_by_count":60,"area":null},{"id":"2111.10502","title":"CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation","created":"2021-11-20","cited_by_count":56,"area":null},{"id":"2205.14623","title":"SKFlow: Learning Optical Flow with Super Kernels","created":"2022-05-29","cited_by_count":56,"area":null},{"id":"2104.13918","title":"High-Resolution Optical Flow from 1D Attention and Correlation","created":"2021-04-28","cited_by_count":54,"area":null},{"id":"2104.02166","title":"Learning Optical Flow from a Few Matches","created":"2021-04-05","cited_by_count":53,"area":null},{"id":"2109.04871","title":"Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation","created":"2021-09-10","cited_by_count":50,"area":null},{"id":"2202.00732","title":"IFOR: Iterative Flow Minimization for Robotic Object Rearrangement","created":"2022-02-01","cited_by_count":42,"area":null},{"id":"2207.07522","title":"Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation","created":"2022-07-15","cited_by_count":38,"area":null},{"id":"2506.09278","title":"UFM: A Simple Path towards Unified Dense Correspondence with Flow","created":"2025-06-10","cited_by_count":36,"area":null},{"id":"2202.03857","title":"Learning Optical Flow with Adaptive Graph Reasoning","created":"2022-02-08","cited_by_count":33,"area":null},{"id":"2203.10712","title":"Disentangling Architecture and Training for Optical Flow","created":"2022-03-21","cited_by_count":32,"area":null},{"id":"2303.11629","title":"TMA: Temporal Motion Aggregation for Event-based Optical Flow","created":"2023-03-21","cited_by_count":32,"area":null},{"id":"2103.04570","title":"Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing","created":"2021-03-08","cited_by_count":31,"area":null},{"id":"2111.14562","title":"Instance-wise Occlusion and Depth Orders in Natural Scenes","created":"2021-11-29","cited_by_count":31,"area":null},{"id":"2203.15089","title":"Learning Optical Flow, Depth, and Scene Flow without Real-World Labels","created":"2022-03-28","cited_by_count":31,"area":null},{"id":"2211.13726","title":"Lightweight Event-based Optical Flow Estimation via Iterative Deblurring","created":"2022-11-24","cited_by_count":31,"area":null},{"id":"2401.16122","title":"DeFlow: Decoder of Scene Flow Network in Autonomous Driving","created":"2024-01-29","cited_by_count":30,"area":null},{"id":"2503.14880","title":"DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework","created":"2025-03-19","cited_by_count":30,"area":null},{"id":"2103.04524","title":"FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation","created":"2021-03-08","cited_by_count":28,"area":null},{"id":"2103.02772","title":"DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images","created":"2021-03-04","cited_by_count":26,"area":null},{"id":"2105.04459","title":"ICON: Learning Regular Maps Through Inverse Consistency","created":"2021-05-10","cited_by_count":26,"area":null},{"id":"2303.11011","title":"Learning Optical Flow from Event Camera with Rendered Dataset","created":"2023-03-20","cited_by_count":26,"area":null},{"id":"2309.15082","title":"RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation","created":"2023-09-26","cited_by_count":26,"area":null},{"id":"2204.07262","title":"Imposing Consistency for Optical Flow Estimation","created":"2022-04-14","cited_by_count":24,"area":null},{"id":"2207.11075","title":"RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos","created":"2022-07-22","cited_by_count":22,"area":null},{"id":"2308.13133","title":"AccFlow: Backward Accumulation for Long-Range Optical Flow","created":"2023-08-25","cited_by_count":19,"area":null},{"id":"2309.16217","title":"GAFlow: Incorporating Gaussian Attention into Optical Flow","created":"2023-09-28","cited_by_count":18,"area":null},{"id":"2103.13578","title":"Test-Time Training for Deformable Multi-Scale Image Registration","created":"2021-03-25","cited_by_count":17,"area":null},{"id":"2104.06789","title":"VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals","created":"2021-04-14","cited_by_count":17,"area":null},{"id":"2206.08572","title":"Enhanced Bi-directional Motion Estimation for Video Frame Interpolation","created":"2022-06-17","cited_by_count":17,"area":null},{"id":"2201.04329","title":"Neural Residual Flow Fields for Efficient Video Representations","created":"2022-01-12","cited_by_count":16,"area":null},{"id":"2202.00909","title":"CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving","created":"2022-02-02","cited_by_count":16,"area":null},{"id":"2304.03560","title":"DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium","created":"2023-04-07","cited_by_count":16,"area":null},{"id":"2103.13725","title":"GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning","created":"2021-03-25","cited_by_count":15,"area":null},{"id":"2308.07207","title":"FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow","created":"2023-08-14","cited_by_count":15,"area":null},{"id":"2311.02661","title":"CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning","created":"2023-11-05","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":"2303.14078","title":"DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling","created":"2023-03-24","cited_by_count":14,"area":null},{"id":"2103.15573","title":"HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences","created":"2021-03-29","cited_by_count":12,"area":null},{"id":"2301.00965","title":"OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup","created":"2023-01-03","cited_by_count":12,"area":null},{"id":"2311.17099","title":"StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences","created":"2023-11-28","cited_by_count":12,"area":null},{"id":"2407.10831","title":"Temporal Event Stereo via Joint Learning with Stereoscopic Flow","created":"2024-07-15","cited_by_count":12,"area":null},{"id":"2104.04724","title":"Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds","created":"2021-04-10","cited_by_count":11,"area":null},{"id":"2211.05709","title":"AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies","created":"2022-11-10","cited_by_count":11,"area":null},{"id":"2209.06918","title":"NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter","created":"2022-09-14","cited_by_count":10,"area":null},{"id":"2301.10018","title":"GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning","created":"2023-01-23","cited_by_count":10,"area":null},{"id":"2303.12017","title":"Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion","created":"2023-03-21","cited_by_count":10,"area":null},{"id":"2312.03102","title":"Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI","created":"2023-12-05","cited_by_count":10,"area":null},{"id":"2203.12835","title":"Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation","created":"2022-03-24","cited_by_count":9,"area":null},{"id":"2207.10314","title":"Semi-Supervised Learning of Optical Flow by Flow Supervisor","created":"2022-07-21","cited_by_count":9,"area":null},{"id":"2207.12163","title":"Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation","created":"2022-07-25","cited_by_count":9,"area":null},{"id":"2303.06209","title":"SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving","created":"2023-03-10","cited_by_count":9,"area":null},{"id":"2305.07853","title":"EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss","created":"2023-05-13","cited_by_count":9,"area":null},{"id":"2312.03790","title":"Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume","created":"2023-12-06","cited_by_count":9,"area":null},{"id":"2104.03560","title":"ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling","created":"2021-04-08","cited_by_count":8,"area":null},{"id":"2205.07399","title":"SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration","created":"2022-05-15","cited_by_count":8,"area":null},{"id":"2208.00776","title":"Deep 360$^\\circ$ Optical Flow Estimation Based on Multi-Projection Fusion","created":"2022-07-27","cited_by_count":8,"area":null},{"id":"2310.17294","title":"Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution","created":"2023-10-26","cited_by_count":8,"area":null},{"id":"2401.00833","title":"Rethinking RAFT for Efficient Optical Flow","created":"2024-01-01","cited_by_count":8,"area":null},{"id":"2405.02608","title":"UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model","created":"2024-05-04","cited_by_count":8,"area":null},{"id":"2306.05691","title":"DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow","created":"2023-06-09","cited_by_count":7,"area":null},{"id":"2306.08887","title":"SplatFlow: Learning Multi-frame Optical Flow via Splatting","created":"2023-06-15","cited_by_count":7,"area":null},{"id":"2308.01568","title":"MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior","created":"2023-08-03","cited_by_count":7,"area":null},{"id":"2309.06714","title":"MPI-Flow: Learning Realistic Optical Flow with Multiplane Images","created":"2023-09-13","cited_by_count":7,"area":null},{"id":"2310.13912","title":"Learning Motion Refinement for Unsupervised Face Animation","created":"2023-10-21","cited_by_count":7,"area":null},{"id":"2403.10425","title":"NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices","created":"2024-03-15","cited_by_count":7,"area":null},{"id":"2107.10004","title":"Deep Iterative 2D/3D Registration","created":"2021-07-21","cited_by_count":6,"area":null},{"id":"2404.17159","title":"Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration","created":"2024-04-26","cited_by_count":6,"area":null},{"id":"2409.04243","title":"Hybrid Cost Volume for Memory-Efficient Optical Flow","created":"2024-09-06","cited_by_count":6,"area":null},{"id":"2308.07104","title":"FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving","created":"2023-08-14","cited_by_count":5,"area":null},{"id":"2410.20752","title":"Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding","created":"2024-10-28","cited_by_count":5,"area":null},{"id":"2503.06992","title":"Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow","created":"2025-03-10","cited_by_count":5,"area":null},{"id":"2107.10990","title":"Detail Preserving Residual Feature Pyramid Modules for Optical Flow","created":"2021-07-23","cited_by_count":4,"area":null},{"id":"2109.15321","title":"Sensor-Guided Optical Flow","created":"2021-09-30","cited_by_count":4,"area":null},{"id":"2111.01261","title":"Joint Detection of Motion Boundaries and Occlusions","created":"2021-11-01","cited_by_count":4,"area":null},{"id":"2111.08485","title":"Consistent Semantic Attacks on Optical Flow","created":"2021-11-16","cited_by_count":4,"area":null},{"id":"2304.12035","title":"GRIG: Few-Shot Generative Residual Image Inpainting","created":"2023-04-24","cited_by_count":4,"area":null},{"id":"2409.17001","title":"Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation","created":"2024-09-25","cited_by_count":4,"area":null},{"id":"2510.04111","title":"Learning Efficient Meshflow and Optical Flow from Event Cameras","created":"2025-10-05","cited_by_count":4,"area":null},{"id":"2101.06333","title":"Optical Flow Estimation via Motion Feature Recovery","created":"2021-01-16","cited_by_count":3,"area":null},{"id":"2102.06979","title":"Normalized Convolution Upsampling for Refined Optical Flow Estimation","created":"2021-02-13","cited_by_count":3,"area":null},{"id":"2201.11407","title":"Non-linear Motion Estimation for Video Frame Interpolation using Space-time Convolutions","created":"2022-01-27","cited_by_count":3,"area":null},{"id":"2208.02305","title":"Unsupervised Flow Refinement near Motion Boundaries","created":"2022-08-03","cited_by_count":3,"area":null},{"id":"2406.07667","title":"PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow","created":"2024-06-11","cited_by_count":3,"area":null},{"id":"2510.13317","title":"Removing Cost Volumes from Optical Flow Estimators","created":"2025-10-15","cited_by_count":3,"area":null},{"id":"2102.00364","title":"OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow","created":"2021-01-31","cited_by_count":2,"area":null}],"next_cursor":"100"}