Papers with Code 아카이브
Action Classification
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- 평가표
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- 보존된 평가 기록
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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/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/BB-Repos/BBaction
Video Classification with Channel-Separated Convolutional Networks
https://github.com/BalajiAJ/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Bangbangbanana/r2plus1d_mindspore
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BelixRogner/SpeedChallenge
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BoChenUIUC/YOWO
https://github.com/Burf/SwinTransformer-Tensorflow2
https://github.com/Burf/tfdetection
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Chaolei98/FreeZAD
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/ChetanTayal138/HAR-Web
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/Chianugoogidi/X3D-tf
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/ChriXiang/GloRe_pytorch
https://github.com/ChristophReich1996/Swin-Transformer-V2
https://github.com/CrazySherman/goodlife
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/Deeksha-Pandit/EVA4-S10
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/EquipoVandV/mmactionVandV
https://github.com/FrederikSchorr/sign-language
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/HoganZhang/weakly-action-localization
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
https://github.com/HsinYingLee/OPN
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/InfoX-SEU/DAE-AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/InfoX-SEU/DAE_AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/Intoxillectual/Monocular-Depth-Estimation-using-DenseNet169
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/JahnaviRamagiri/DenseDepth_1
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/JeffCHEN2017/WSSTG
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/JiaPeng1234/MRI-Segmentation-Transformer
https://github.com/JuliBaCSE/WeCare_makeathon
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/JunnYu/x-transformers-paddle
https://github.com/JunweiLiang/aicity_action
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/KSonPham/ViVit-a-Pytorch-implementation
https://github.com/KarthikGangadhar/depth-estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/KiUngSong/Vision
https://github.com/KingGugu/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/LRacoci/permutation-graphml
https://github.com/Lin716/STPN-lin
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.