Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 11
- 보존된 평가 기록
- 81
Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Wuxiao85/paddle_EfficientGCNv
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/XinzeWu/st-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/YHKimGithub/FATSnet
Temporally smooth online action detection using cycle-consistent future anticipation
https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yang2446/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YangRui2015/Modular_HER
https://github.com/Yindong-Zhang/myGAT
https://github.com/YiruS/pointnet_adversarial
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YoUNG824/DDPG
https://github.com/Young98CN/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Ysp9714/SlowFastNet-keras
https://github.com/YunseobShin/wiki_GAT
https://github.com/Yuto0107/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ZainRaza14/deepRL
https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ZhangNYG/BSN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/ZhangNYG/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ZiyangY/IndProject-RL-in-Supply-chain
https://github.com/abbadka/quadcopter
https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/abhilash1910/GraphAttentionNetworks
https://github.com/abi-kothapalli/minimumdominatingsets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/achalddave/segment-any-moving
https://github.com/agethen/RPAN
https://github.com/ahmed-anas/thesis-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ahsaniqbal/Kinetics-FeatureExtractor
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ai-winter/python_motion_planning
https://github.com/aidarikako/JMDA
https://github.com/ailab-pku/rl-framework
https://github.com/aim3-ruc/youmakeup_challenge2022
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/aimerykong/predictive-filter-flow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.