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/ftdlyc/pointnet_pytorch

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

https://github.com/fxia22/pointnet.pytorch

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

https://github.com/gabbygab1233/Image-Anonymization

Learning to Anonymize Faces for Privacy Preserving Action Detection

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gauthamkrishna-g/ActiveHARNet

ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/github-zbx/ST-GCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/github-zbx/ava_datasets

AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmum/umwpl2021

Graph Attention Networks

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

View-Invariant Probabilistic Embedding for Human Pose

https://github.com/google/next-prediction

From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/gsig/charades-algorithms

Asynchronous Temporal Fields for Action Recognition

https://github.com/guilk/diva

BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition

Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

https://github.com/guillaumeboniface/reacher

Continuous control with deep reinforcement learning

https://github.com/gwthomas/gtml

Continuous control with deep reinforcement learning

https://github.com/h-aboutalebi/SparceReward

Continuous control with deep reinforcement learning

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamishs/JAX-RL

Continuous control with deep reinforcement learning

https://github.com/handasontam/GAT-with-edgewise-attention

Graph Attention Networks

https://github.com/happygds/two_level

Temporal Action Detection with Structured Segment Networks

https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/hassony2/kinetics_i3d_pytorch

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/hazdzz/ChebyNet

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

https://github.com/hazdzz/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/hazdzz/SGC

Simplifying Graph Convolutional Networks

https://github.com/helloxy96/CS5242_Project2020

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/hemilpanchiwala/Hindsight-Experience-Replay

Continuous control with deep reinforcement learning

https://github.com/hhe-distance/AIF-CNN

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

https://github.com/hill-a/stable-baselines

Continuous control with deep reinforcement learning

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