https://github.com/hjeun/idu
Learning to Discriminate Information for Online Action Detection
Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/hyperfraise/end-to-end-BSN.pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/hyperparameters/tracking_via_colorization
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement
https://github.com/iamjeff7/j-va-aagcn
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
https://github.com/ibrahimalimetin/HDAD-iGAV-Dataset-and-its-Benchmarking-GUI-with-Deep-Learning-Techniques-for-HAR
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
https://github.com/idiap/atco2-corpus
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/isotlaboratory/ml4vrp
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ivrit-ai/ivrit.ai
ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development
https://github.com/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py
https://github.com/jbistanbul/miniroad
https://github.com/jiang-yidi/ts-talknet
Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection
https://github.com/jiangboahu/glcn-tf
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/jihun-kr/SlowFast
https://github.com/joaopedromattos/gnee
https://github.com/jonmun/EPIC-KITCHENS-100_UDA_TA3N
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/jwwoody/mlb-deeplearning
https://github.com/jwwoody/mlb-youtube
https://github.com/kaistmm/TalkNCE
LoCoNet: Long-Short Context Network for Active Speaker Detection
https://github.com/kchengiva/Shift-GCN
https://github.com/ken724049/action-recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kenziyuliu/DGNN-PyTorch
https://github.com/kiharalab/gnn_pocket
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/kkahatapitiya/X3D-Multigrid
https://github.com/krasing/DRLearningCollaboration
https://github.com/krasing/DRLearningContinuousControl
https://github.com/kushagra06/DDPG
https://github.com/kylemin/S3D
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.