Papers with Code 아카이브
Action Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Image source: The Kinetics Human Action Video Dataset
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/terrychenism/OctaveConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/thaitruongan/camera-surveillance-ai
https://github.com/thiagorjes/depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/tianfr/mononerf
https://github.com/tianfr/semantic-flow
https://github.com/tinymilky/RSANet
https://github.com/tonysy/PyAction
https://github.com/towhee-io/towhee
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/tuanzhangCS/octconv_resnet
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/txyugood/PaddleMVF
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
https://github.com/vikramjit-sidhu/hlcv_project
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/visaVita/mmaction2
https://github.com/vivym/OctaveConv.pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/w-sugar/Video-Swin-Transformer
https://github.com/wangjk666/stts
https://github.com/wangxiang1230/SSTAP
https://github.com/wasilone11/ICPR-RIP-2024
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/wei-tim/YOWO
https://github.com/whwu95/Cap4Video
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/whwu95/DSANet
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/whwu95/GPT4Vis
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/woodfrog/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/x-plug/mplug-owl
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/xiuyu0000/new_papers_codes/tree/main/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/r2plus1d.py
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/xu3kev/octconv-chainer
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/yangyucheng000/glore_res200
https://github.com/yaohungt/GSTEG_CVPR_2019
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.