https://github.com/2023-MindSpore-4/Code8/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
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/2023-MindSpore-4/Code8/tree/main/CascadeRCNN
Context-Aware RCNN: A Baseline for Action Detection in Videos
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code5/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2024-MindSpore-1/Code6/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2024-MindSpore-1/Code6/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2024-MindSpore-1/Code6/tree/main/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/2024-MindSpore-1/Code7/tree/main/CascadeRCNN
Context-Aware RCNN: A Baseline for Action Detection in Videos
https://github.com/2024-MindSpore-1/Code9/tree/main/LOGO--Mindspore-master
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/404nofound/gat
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/6210612757/facerecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/AD2605/Action-Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/AI-Guru/pointcloud_experiments
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AKASH2907/Content-based-Video-Recommendation
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/AKASH2907/Content-based-Video-Relevance-Prediction
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/AbdalaDiasse/Video-classification-for-oil-quality-estimation
https://github.com/Abdulrahman-Adel/Real-Life-Violence-Detection
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/Alexyuda/action_recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/AlfredoZermini/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Alirezarahnamaa/Adaptive-Frame-Selection-Algorithm
https://github.com/Alirezarahnamaa/Temporal-Relations-of-Informative-Frames-in-Action-Recognition
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Amir-Hofo/CGAN_MNIST_Pytorch
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Anak2016/GAT
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Andrew-booler/BSN-ActivityNet
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.