Papers with Code 아카이브

Action Detection

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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/doronharitan/human_activity_recognition_LRCN

UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

https://github.com/dpoulopoulos/drl_collaborate_compete

Continuous control with deep reinforcement learning

https://github.com/dpoulopoulos/drl_continuous_control

Continuous control with deep reinforcement learning

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dwtstore/sfm1

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/dyth/doublegum

Continuous control with deep reinforcement learning

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/dzwallkilled/IEforAR

Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

https://github.com/eladb3/orvit

Object-Region Video Transformers

https://github.com/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/epic-kitchens/C1-Action-Recognition-TSN-TRN-TSM

Rescaling Egocentric Vision

https://github.com/epic-kitchens/epic-kitchens-slowfast

Rescaling Egocentric Vision

https://github.com/ericksiavichay/cs230-final-project

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/eth212/Qmind_ModelNet10_PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/facebookresearch/DomainBed

Improve Unsupervised Domain Adaptation with Mixup Training

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/pytorchvideo

SlowFast Networks for Video Recognition

https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

https://github.com/fangwei123456/spikingjelly

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fdcl-gwu/gym-rotor

Continuous control with deep reinforcement learning

https://github.com/fdsa1860/skeletal

https://github.com/feruxhi/thoughts

Continuous control with deep reinforcement learning

https://github.com/fhbzc/FishAgentSimulation

Continuous control with deep reinforcement learning

https://github.com/fiberleif/nc_ddpg

Continuous control with deep reinforcement learning

https://github.com/firework8/ProtoGCN

Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition

https://github.com/flavioschneider/ml_papers_presentations

Continuous control with deep reinforcement learning

https://github.com/floodsung/DDPG

Continuous control with deep reinforcement learning

https://github.com/flowersteam/curious

Continuous control with deep reinforcement learning

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/frans-db/progress-prediction

Am I Done? Predicting Action Progress in Videos

https://github.com/freddieee/pn_6d_single

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.