https://github.com/Mind23-2/MindCode-62
Video Classification with Channel-Separated Convolutional Networks
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/Mind23-2/MindCode-63
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Mind23-2/MindCode-87
https://github.com/Mind23-2/MindCode-88/tree/main/slowfast
https://github.com/MindCode-4/code-1/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindCode-4/code-11/tree/main/delving-deeper-into-convolutional
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindCode-4/code-13/tree/main/refining-activation
https://github.com/MindCode-4/code-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindCode-4/code-5/tree/main/vit_mae
https://github.com/MindCode-4/code-6/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindCode-4/code-8/tree/main/refining-activation
https://github.com/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d
Video Classification with Channel-Separated Convolutional Networks
https://github.com/MindSpore-paper-code-2/code3/tree/main/stgcn
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
https://github.com/MindSpore-paper-code-2/code3/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/MindSpore-paper-code-2/code3/tree/main/trn
https://github.com/MindSpore-paper-code-2/code3/tree/main/tsn
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code10/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code4/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code6/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/MindSpore-paper-code-3/code6/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/MindSpore-paper-code-3/code6/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/MindSpore-paper-code-3/code7/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/dpn
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-scientific-2/code-10/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific-2/code-12/tree/main/LGL-INR-master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.