https://github.com/ZJCV/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Papers with Code 아카이브
Action Recognition In Videos
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Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZJUT-ERCISS/c3d_mindspore
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/ahmetgunduz/Real-time-GesRec
https://github.com/aim3-ruc/youmakeup_challenge2022
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/aj9011/Car-Speed-Prediction
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/antoninofurnari/rulstm
https://github.com/arunos728/arunos728.github.io
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
https://github.com/ashu5711/Neural_Network_Hand_Gesture_Recognition
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/axon-research/c3d-keras
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/ayushrox/TSN_Colab
https://github.com/boseaslcohort/youtube-8m
https://github.com/bruceyo/MMNet
https://github.com/bruceyo/TSMF
https://github.com/bryanyzhu/two-stream-pytorch
https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need
https://github.com/chunfeng0301/Flow
https://github.com/code-implementation1/Code2/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/coderSkyChen/Action_Recognition_Zoo
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/corcovadoming/mpnnel
https://github.com/craftGBD/caffe-GBD
https://github.com/croros/STAR_Network_Pytorch
https://github.com/damien911224/theWorldInSafety
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/daniel-code/TubeViT
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
https://github.com/dipika-singhania/multi-scale-action-banks
Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dluvizon/deephar
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
https://github.com/doronharitan/human_activity_recognition_LRCN
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/epic-kitchens/C1-Action-Recognition-TSN-TRN-TSM
https://github.com/epic-kitchens/annotations
https://github.com/epic-kitchens/epic-kitchens-55-annotations
https://github.com/epic-kitchens/epic-kitchens-slowfast
https://github.com/facebookresearch/R2Plus1D
What Makes Training Multi-Modal Classification Networks Hard?
https://github.com/facebookresearch/VMZ
What Makes Training Multi-Modal Classification Networks Hard?
https://github.com/facebookresearch/pytorchvideo
https://github.com/gaoqianmiao/Summer-project
https://github.com/gongda0e/futr
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.