https://github.com/LouisDumont/GCN---re-implementation
Semi-Supervised Classification with Graph Convolutional Networks
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 4
- 보존된 평가 기록
- 10
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/LukasHedegaard/co3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Lw510107/pointnet-2018.6.27-
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MS-Mind/MS-Code-01/tree/main/mass
https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local
https://github.com/MS-Mind/MS-Code-08/tree/main/Non_local
https://github.com/MS-P3/code7/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MS-P3/code7/tree/main/vivit
https://github.com/MakeDirtyCode/cDCGAN-celebA-pytorch
https://github.com/ManjunathAdi/Seq2Seq_RNN
https://github.com/MarcRoigVilamala/DeepProbCEP
https://github.com/MarkoLewis-Projects/Sign_language_detection
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/MasoumehVahedi/GANs-Model
https://github.com/Maxchengqin/DSCNet
https://github.com/Maxchengqin/MSSTNet_2D_3D
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Conditional_Generative_Adversarial_Nets
https://github.com/Mayurji/Image-Classification-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints
https://github.com/MekkaSiekka/C3D-UCF11-Tensorflow
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Messham87/COVID_GAT
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/MikeG112/RFSBD
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-101/tree/main/I3D
https://github.com/Mind23-2/MindCode-101/tree/main/MVD
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-11
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/Mind23-2/MindCode-3/tree/main/MVD
https://github.com/Mind23-2/MindCode-30
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/Mind23-2/MindCode-49
https://github.com/Mind23-2/MindCode-5/tree/main/psenet
https://github.com/Mind23-2/MindCode-50
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.