https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/r2plus1d.py
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xjtupanda/ACGNet
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/xlliu7/BMN-Boundary-Matching-Network
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/xrenaa/Human-Motion-Analysis-with-Deep-Metric-Learning
https://github.com/xuhongzuo/DeepOD
https://github.com/xurui1217/pointnet.pytorch-master
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xxcheng0708/BSNPlusPlus-boundary-sensitive-network
https://github.com/y2kmz/pointnetv2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yCobanoglu/infinite-width-gnns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangjun1994/CAGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangyucheng000/Paper-3/tree/main/opendmc
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
https://github.com/yangyucheng000/glore_res200
https://github.com/yangyucheng000/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yangyucheng000/unipose-mindspore
UniPose: Unified Human Pose Estimation in Single Images and Videos
https://github.com/yanhuchen/quantum-graph-convolutional-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yanx27/Pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yanxp/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yaohungt/GSTEG_CVPR_2019
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/yczhang1017/SSD_resnet_pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ydtydr/hyla
https://github.com/ydzhang-stormstout/HAT
https://github.com/yevgm/deepadv3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yfsong0709/ResGCNv1
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/yfxc/tensorflow-T3D
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://github.com/yiskw713/ClassActivationMapping
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yiskw713/SmoothGradCAMplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yiskw713/scorecam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yjzhai-cs/vehicle-10
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ykrmm/sota_gnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yqyao/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ytng001/sensemaking
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yumaloop/LSTMAutoEncoderOnMovingMNIST
https://github.com/yunqing-me/attackvlm
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yururyuru00/GAT_pyG
https://github.com/yuvrajarora/CSCI5922_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.