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/hitachinsk/fgt
Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting
https://github.com/hitachinsk/isvi
Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting
https://github.com/hpcforge/gmflow
https://github.com/hpcforge/raft
https://github.com/hperrot/splatting
https://github.com/huggingface/transformers
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/hyperparameters/tracking_via_colorization
https://github.com/hzwer/Practical-RIFE
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/hzwer/Pratical-RIFE
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/hzwer/eccv2022-rife
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/ibaiGorordo/NeuFlow_v2-Pytorch-Inference
NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
https://github.com/ibaiGorordo/ONNX-NeuFlowV2-Optical-Flow
NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
https://github.com/irfanICMLL/ETC-Real-time-Per-frame-Semantic-video-segmentation
Efficient Semantic Video Segmentation with Per-frame Inference
https://github.com/jcao216/DAIN_Modified
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/jgorgenucsd/corr_tf
https://github.com/jiaxinxie97/Video-depth-estimation
https://github.com/jinsc37/DIFRINT
Deep Iterative Frame Interpolation for Full-frame Video Stabilization
https://github.com/jonmun/MM-SADA-code
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
https://github.com/kaichen-z/rad
Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes
https://github.com/keeper121/liteflownet-tf2
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
https://github.com/khalidw/MOT16_Annotator
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
https://github.com/kimwoojoo/edit_multiflow
https://github.com/krasserm/perceiver-io
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/kritiksoman/GIMP-ML
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/kuangzijian/Flow-Based-Video-Matting
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/lab-notebooks/jobrunner
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
https://github.com/laomao0/BIN
https://github.com/laoshiwei/face-anti-spoofing
Exploiting temporal and depth information for multi-frame face anti-spoofing
https://github.com/large-trajectory-model/atm
https://github.com/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/lhaippp/GyroFlow-PyTorch
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
https://github.com/lhaof/Salient-Object-Detection-Papers
https://github.com/lhaof/fast-salient-object-detection
https://github.com/lhoangan/eden-generation
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.