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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/shenweichen/GraphNeuralNetwork

Graph Attention Networks

https://github.com/shi27feng/transformers.satisfy

Graph Attention Networks

https://github.com/shlizee/Predict-Cluster

PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition

https://github.com/simonmeister/UnFlow

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/siyuofzhou/DDPG

Continuous control with deep reinforcement learning

https://github.com/sliao-mi-luku/DeepRL-multiple-agents-tennis-udacity-drlnd-p3

Continuous control with deep reinforcement learning

https://github.com/small-whirlwind/EfficientGCN_paddle

Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition

https://github.com/smfirmin/DeepLearningFinalProject

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

https://github.com/sming256/AdaTAD

End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames

https://github.com/snowkylin/gnn

Graph Attention Networks

https://github.com/snownus/COOP

Graph Attention Networks

https://github.com/songrotek/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/point-cloud-segmentation

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

https://github.com/sssste/tridet

Temporal Action Localization with Enhanced Instant Discriminability

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/stillarrow/S2VT_ACT

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/stnoah1/infogcn

https://github.com/subercui/pyGConvAT

Graph Attention Networks

https://github.com/sulasen/race-events-recognition-1

Review of Action Recognition and Detection Methods

https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/susan-amin/SparseBaseline1

Continuous control with deep reinforcement learning

https://github.com/syyeung/frameglimpses

End-to-end Learning of Action Detection from Frame Glimpses in Videos

https://github.com/taishan1994/pytorch_gat

Graph Attention Networks

https://github.com/taku-y/20181125-pybullet

Continuous control with deep reinforcement learning

https://github.com/taoruijie/talknet-asd

Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection

https://github.com/tatp22/pytorch-fast-GAT

Graph Attention Networks

https://github.com/taylorlu/AudioKWS

An End-to-End Architecture for Keyword Spotting and Voice Activity Detection

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnet

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/tensorflow/models

AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions

https://github.com/tensorlayer/RLzoo

Continuous control with deep reinforcement learning

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/thanhtrunghuynh93/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

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