https://github.com/MindSpore-scientific-2/code-3/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
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/MindSpore-scientific-2/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-1/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-10/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
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-13/tree/main/token_learner
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-14/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-2/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-7/tree/main/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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/NSR9/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NiallEHunt/MonocularDepth-Using-LightFields
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Nithin-GK/UniteandConquer
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
https://github.com/Noopuragr/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Nortinwell/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/OanaIgnat/i3d_keras
https://github.com/OlineRanum/SAPA
https://github.com/OpenGVLab/UniFormerV2
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PASSL
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
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/pp-tsn.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/PaulEmmanuelSotir/DeepCV
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/Pilhyeon/Learning-Action-Completeness-from-Points
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
https://github.com/QUVA-Lab/timeception
https://github.com/Qdriving/Swin-TransformerV2-in-PP
https://github.com/RoshanRane/PredNet-and-Predictive-Coding-A-Critical-Review
https://github.com/RoshanRane/segmentation-moving-MNIST
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.