https://github.com/Jia-Research-Lab/Simple-SR
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
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/JimmyChame/The-State-of-the-Art-in-HDR-Deghosting
Attention-guided Network for Ghost-free High Dynamic Range Imaging
https://github.com/KAIST-VICLab/BiM-VFI
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions
https://github.com/KiUngSong/Vision
https://github.com/LijieFan/tvnet
End-to-End Learning of Motion Representation for Video Understanding
https://github.com/Lubsey1/inpainting_colab
https://github.com/MS-Mind/MS-Code-06/tree/main/PoseNet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
https://github.com/Mandroide/gpasb-nvidia2021-track4
Good Practices and A Strong Baseline for Traffic Anomaly Detection
https://github.com/MaterialEyes/Plot2Spec
https://github.com/MaybeShewill-CV/sfnet-tensorflow
https://github.com/McDo/LightFlowPytorch
https://github.com/MegEngine/arXiv2020-RIFE
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Mind23-2/MindCode-4/tree/main/densenet
https://github.com/Mind23-2/MindCode-5/tree/main/PWCNet
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/MindCode-4/code-12/tree/main/locally-consistent-deformable-convolution
https://github.com/MindCode-4/code-2/tree/main/perceiver
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/MindCode-4/code-7/tree/main/locally-consistent-deformable-convolution
https://github.com/MindCode-4/code-8/tree/main/robustness_depth_lang
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
https://github.com/MindCode-4/code-9/tree/main/robustness_depth_lang
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
https://github.com/MindSpore-paper-code-2/code2/tree/main/PWCNet
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/MindSpore-paper-code-2/code2/tree/main/PoseNet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
https://github.com/MindSpore-paper-code-3/code10/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code4/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code7/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/dgcnet_res101
https://github.com/MindSpore-scientific-2/code-7/tree/main/SISDTA
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/MindSpore-scientific/code-12/tree/main/GAF
https://github.com/MindSpore-scientific/code-14/tree/main/robustness_depth_lang
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
https://github.com/MindSpore-scientific/code-14/tree/main/smu
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
https://github.com/MindSpore-scientific/code-5/tree/main/RAF-ms-main
https://github.com/MohamedAliRashad/Crystal
https://github.com/MoonBlvd/tad-IROS2019
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
https://github.com/MoonBlvd/tad-IROS2019-TBD-
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
https://github.com/MurrayC7/PWC-Net
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/NVIDIA/flownet2-pytorch
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.