https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Papers with Code 아카이브
Temporal Action Localization
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/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/812618101/TAL-Demo
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
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/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AbdalaDiasse/Video-classification-for-oil-quality-estimation
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AbertayMachineLearningGroup/machine-learning-SIEM-water-infrastructure
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning
https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/AlexMRuch/Can-x2vec-Save-Lives
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/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/Anak2016/GAT
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Andrew-booler/BSN-ActivityNet
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Anou9531/GAT
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BIG-S2/keras-gnm
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Bangbangbanana/r2plus1d_mindspore
A Closer Look at Spatiotemporal Convolutions for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.