https://github.com/2023-MindSpore-4/Code6/tree/main/PWCNet
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
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/2024-MindSpore-1/Code3/tree/main/Flow
https://github.com/2796gaurav/code_examples/tree/main/Perceiver
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/812618101/TAL-Demo
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
https://github.com/89viper/Python-Deep_Video_Inpainting
https://github.com/AMatusV/MaskFlownet-ROS
MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
https://github.com/AaltoVision/DGC-Net
https://github.com/AbdalaDiasse/Video-classification-for-oil-quality-estimation
Hidden Two-Stream Convolutional Networks for Action Recognition
https://github.com/AdarshMJ/PredGAN
https://github.com/ArchieMeng/rife-ncnn-vulkan-python
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/BehradToghi/ConvLSTM_VOS
https://github.com/BehradToghi/ECCV_Youtube_VOS
https://github.com/BestJuly/IIC
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
https://github.com/BestJuly/VCP
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
https://github.com/BurguerJohn/Dain-App
https://github.com/ChristophReich1996/SMURF
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
https://github.com/ClementPinard/FlowNetPytorch
https://github.com/ClementPinard/Pytorch-Correlation-extension
https://github.com/Coldog2333/pytoflow
https://github.com/Cyril-Sterling/EVFlowNet-pytorch
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
https://github.com/Dartum08/Video-Anomaly-Detection
https://github.com/DepthAnything/Video-Depth-Anything
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
https://github.com/Dragoniss/minspore-phase2-E2FGVI
Towards An End-to-End Framework for Flow-Guided Video Inpainting
https://github.com/ElliotHYLee/VisualOdometry3D
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/Fanziapril/mvfnet
https://github.com/Flunzmas/vp-suite
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
https://github.com/Genera1Z/SAVi-PyTorch
https://github.com/Guillem96/spynet-pytorch
https://github.com/HKBU-HPML/FADNet
https://github.com/HKBU-HPML/IRS
https://github.com/HKUST-NISL/MIMAMO-Net
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
https://github.com/HastingsGreer/InverseConsistency
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
https://github.com/HsinYingLee/OPN
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/HyeongminLEE/pytorch-sepconv
Video Frame Interpolation via Adaptive Separable Convolution
https://github.com/Hzzone/Precipitation-Nowcasting
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.