https://github.com/watonomous/action-classification
공식 코드RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow
Papers with Code 아카이브
Action Classification
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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/wgcban/adamae
공식 코드AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
https://github.com/whwu95/ATM
공식 코드What Can Simple Arithmetic Operations Do for Temporal Modeling?
https://github.com/whwu95/BIKE
공식 코드https://github.com/whwu95/MVFNet
공식 코드MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/whwu95/text4vis
공식 코드Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/xlliu7/E2E-TAD
공식 코드https://github.com/yang-song/APDLM
공식 코드https://github.com/yiskw713/asrf
공식 코드Alleviating Over-segmentation Errors by Detecting Action Boundaries
https://github.com/yjxiong/caffe
공식 코드Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/yjxiong/temporal-segment-networks
공식 코드Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/youshyee/CEP
공식 코드https://github.com/zhengshou/scnn
공식 코드Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
https://github.com/zhujiagang/gating-ConvNet-code
공식 코드Learning Gating ConvNet for Two-Stream based Methods in Action Recognition
https://github.com/2023-MindSpore-1/ms-code-187
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet3d
https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/2023-MindSpore-1/ms-code-68
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsn
https://github.com/2023-MindSpore-4/Code-5/tree/main/MVD
https://github.com/2023-MindSpore-4/Code10/tree/main/glore_res
https://github.com/2023-MindSpore-4/Code11/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/swin
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/3dperceptionlab/visual-wetlandbirds
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/812618101/TAL-Demo
Deep Concept-wise Temporal Convolutional Networks for Action Localization
https://github.com/AD2605/Action-Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/Abdulrahman-Adel/Real-Life-Violence-Detection
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/AlexMRuch/Can-x2vec-Save-Lives
https://github.com/Alexyuda/action_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AmirsSaad/UWPhysDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/AnjieZheng/OctaveConv-Pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.