https://gitlab.engr.illinois.edu/hubris/mesrnn
공식 코드Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
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://gitlab.uni-koblenz.de/dwete/ldatopicmodels
공식 코드https://huggingface.co/datasets/SoftMINER-Group/NicheHazardQA
공식 코드Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
https://huggingface.co/datasets/SoftMINER-Group/TechHazardQA
공식 코드https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://gitee.com/zhengkeli/zkl-knowedit-rome
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/1zgh/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2023-MindSpore-1/ms-code-187
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stgcn
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/2023-MindSpore-1/ms-code-35
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-55
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-68
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsn
https://github.com/2023-MindSpore-4/Code-5/tree/main/MVD
https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code10/tree/main/glore_res
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code7/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/2023-MindSpore-4/Code8/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-4/Code8/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.