https://github.com/nowsyn/DVM
공식 코드Deep Video Matting via Spatio-Temporal Alignment and Aggregation
Papers with Code 아카이브
Optical Flow Estimation
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Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nsn-hello/geoflow-slam
공식 코드GeoFlow-SLAM: A Robust Tightly-Coupled RGBD-Inertial Fusion SLAM for Dynamic Legged Robotics
https://github.com/nust-machine-intelligence-laboratory/hcpn
공식 코드Co-attention Propagation Network for Zero-Shot Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/hgpu
공식 코드https://github.com/opennlplab/avs-bidirectional
공식 코드Improving Audio-Visual Segmentation with Bidirectional Generation
https://github.com/ostadabbas/infant-respiration-estimation
공식 코드https://github.com/ostadabbas/nns-detection-and-segmentation
공식 코드Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental Cue
https://github.com/owl-project/NVISII
공식 코드NViSII: A Scriptable Tool for Photorealistic Image Generation
https://github.com/ozguncck/recovering-the-imperfect
공식 코드Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
https://github.com/ozheng1993/ucf-sst-citysim-dataset
공식 코드CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins
https://github.com/papermsucode/mutual-modality-learning
공식 코드https://github.com/patrykcieslak/stonefish
공식 코드Stonefish: Supporting Machine Learning Research in Marine Robotics
https://github.com/pavanravva/enhanced-mmasd
공식 코드https://github.com/pedestriandynamics/cloudfast-dl4pude
공식 코드A Cloud-based Deep Learning Framework for Early Detection of Pushing at Crowded Event Entrances
https://github.com/pgodet/star_flow
공식 코드STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation
https://github.com/phflot/dsd_momag
공식 코드Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
https://github.com/phoenix104104/fast_blind_video_consistency
공식 코드https://github.com/piergiaj/representation-flow-cvpr19
공식 코드https://github.com/pohl-michel/Lucas-Kanade-pyramidal-optical-flow-for-3D-image-sequences
공식 코드https://github.com/ppliuboy/Flow2Stereo
공식 코드Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching
https://github.com/ppliuboy/SelFlow
공식 코드https://github.com/princeton-vl/DeepV2D
공식 코드DeepV2D: Video to Depth with Differentiable Structure from Motion
https://github.com/princeton-vl/RAFT
공식 코드https://github.com/princeton-vl/RAFT-3D
공식 코드https://github.com/princeton-vl/cer-mvs
공식 코드https://github.com/princeton-vl/infinigen
공식 코드https://github.com/princeton-vl/multislam_diffpose
공식 코드Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
https://github.com/princeton-vl/raft-stereo
공식 코드RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
https://github.com/princeton-vl/sea-raft
공식 코드https://github.com/princeton-vl/waft
공식 코드https://github.com/prs-eth/DeFlow
공식 코드https://github.com/pubgeo/monocular-geocentric-pose
공식 코드https://github.com/pzzz-cv/RStab
공식 코드https://github.com/qianqianwang68/omnimotion
공식 코드https://github.com/qianyuzqy/TransVOD_Lite
공식 코드TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
https://github.com/qinghuannn/pamfn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.