Papers with Code 아카이브
Temporal Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 14
- 보존된 평가 기록
- 125
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/fmthoker/severe-benchmark
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/fongyk/graph-attention
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/frankaging/pyvene
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
https://github.com/freddieee/pn_6d_single
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/frgfm/torch-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/frostinassiky/bsp
Boundary-sensitive Pre-training for Temporal Localization in Videos
https://github.com/ftdlyc/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ftorres11/saliencysense
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fxia22/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/galkampel/HyperNetworks
https://github.com/gaoqianmiao/Summer-project
https://github.com/garroud/human-motion-prediction-pytorch
https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset
https://github.com/gayanku/FDGATII
https://github.com/gcucurull/jax-gat
https://github.com/gd-goblin/C-3PO_Motion_Retargeting_Module
https://github.com/gd-goblin/C-3PO_RobotControlModule
https://github.com/ginkyenglee/Explaining_Decision_of_Time_Series_Data
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/ginn24/RefineDet-1
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/github-zbx/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gitzh-chen/rmlr
Riemannian Multinomial Logistics Regression for SPD Neural Networks
https://github.com/giuseppefutia/link-prediction-code
https://github.com/gmum/umwpl2021
https://github.com/goddoe/hide-and-seek
https://github.com/google-deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/google-research/google-research/tree/master/poem
https://github.com/google-research/human-scene-transformer
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/gopi-kampli/ImageClusteringDL
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gordicaleksa/pytorch-GAT
https://github.com/gpspelle/Convert_channel
https://github.com/gpspelle/Crowd-Counting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/griffinbran/machines_best_friend
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gsig/charades-algorithms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.