https://github.com/MindSpore-scientific/code-10/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
Papers with Code 아카이브
Video Recognition
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Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific/code-11/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MohammadRezaQaderi/Video-Swin-Transformer
https://github.com/Oichii/resnet3D_pulse
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md
https://github.com/PaulEmmanuelSotir/DeepCV
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/STONE-SAMA/x3d_mindspore
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/Scalsol/mega.pytorch
https://github.com/SharadGitHub/OctaveUnet
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/Shito0907/3D_resnet
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/Silver-L/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/ThomasEhling/Weight_Standardization
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/TrustAI/DeepConcolic
https://github.com/WavesUR/embedded_TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/Whiffe/mmaction2_YF
https://github.com/Ysp9714/SlowFastNet-keras
https://github.com/ZJCV/X3D
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/acewjh/Video-Swin-Transformer
https://github.com/amohant4/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/andreazuna89/Excitation-Dropout
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
https://github.com/ariG23498/TokenLearner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/braincreators/octconv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need
https://github.com/code-implementation1/Code7/tree/main/resnet3d
https://github.com/code-implementation1/Code8/tree/main/slowfast
https://github.com/d-li14/octconv.pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/ddddwee1/Octave-Convolution
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/deeplearningforfun/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/delta6189/Anime-Sketch-Colorizer
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/doronharitan/human_activity_recognition_LRCN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/facebookresearch/pytorchvideo
https://github.com/gan3sh500/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.