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
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/MindSpore-scientific-2/code-8/tree/main/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindSpore-scientific-2/code-8/tree/main/temporal-normalizing-flows
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
https://github.com/MindSpore-scientific/code-1/tree/main/Simba
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
https://github.com/MindSpore-scientific/code-10/tree/main/Simba
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
https://github.com/MindSpore-scientific/code-11/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-13/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific/code-13/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
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/refining-activation
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-5/tree/main/refining-activation
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/ModelBunker/PointNet-TensorFlow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MohammadRezaQaderi/Video-Swin-Transformer
https://github.com/MohammadrezaNakhaei/FQL
https://github.com/Moon-Xuezha/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MrSyee/rnn_autoenc
https://github.com/Murali81/InfoGAN
https://github.com/NEUdeep/MOC-Detector-Pytorch1.4
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/NVIDIA/flownet2-pytorch
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/NVlabs/STEP
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
https://github.com/NYU-DICE-Lab/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/NathanDeMaria/AugmentedCycleGAN
https://github.com/Natsu6767/Conditional-AnimeGAN
https://github.com/Negus25/GAT-Mindspore
https://github.com/NicolaBernini/PaperAnalysis_ICLR2021_NegativeDataAugmentation
https://github.com/Nikhil9786/Scene-Segmentation-Using-MovieScenes-Dataset
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
https://github.com/Nortinwell/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/OML-Team/open-metric-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Ontheway361/BSN_pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/OpenGVLab/UniFormerV2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.