---
# System prepended metadata

title: GAS-GCN
tags: [GAS GCN, Machine Learning]

---

###### tags: `GAS-GCN`

# Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

## About the paper
- [read](https://www.mdpi.com/1424-8220/20/12/3499)
- author: Wensong Chan ,Zhiqiang Tian, and Yang Wu
- published: 2020/6/21 
- contributin
    - ASGCN: combine structural and implicit edge
    - Gated CNN: filter out useless temporal information
- Dataset: 
    - NTU-RGB + D 120
        - 南洋理工大學蒐集的資料集
        - RGB videos: 1920x1080
        - depth map videos: 512*424
        - IR videos (紅外線): 512*424
        - ==3D== skeletal data: 25 個關節
        - [download](https://github.com/shahroudy/NTURGB-D/tree/master)
    - Kinetics
        - 主要來自Youtube
        - 分類: person, person-person, person-object
        - 至少600個影片，一個影片10秒
        - 有label
        - [download](https://k4kan.medium.com/ntu-rgb-d-%E8%B3%87%E6%96%99%E9%9B%86%E4%BB%8B%E7%B4%B9-718e45554a61)
        - [detail](https://zhuanlan.zhihu.com/p/347490726)
- Experiments Details
    - PyTorch: implement GAS-GCN
    - SGD: optimization strategy
    - 2 NVIDIA 2080TI GPU with memory of 11GB
    -  NTU-RGB + D training
        -  T=300
        -  epoch=50, base learning rate:
            -  1~29: 0.1
            -  30~40: 0.1 x 0.1
            -  41~50: 0.1 x 0.1 x 0.1
        -  batch size=28
    -  Kinetics
        - T=150
        - epoch=65, base learning rate:
            - 1~44: 0.1
            - 45~55: 0.1 x 0.1
            - 56~65: 0.1 x 0.1 x 0.1 
        - batch size=64
        - 𝜇=0.5

## GAS-GCN
- Gated Action-Specific Graph Convolutional Networks
- Why
    - GCNs-based only focus on ajencent matrix (只有附近的joint，但是每個動作不一定只影響到一個關節附近)
    - The connection between two joints is structural edge
    - solution: action-specific graph convolutional module (ASGCM)
        - generate the implicit edge
        - decide the ratio of the combination of structural edge and implicit edges according to different action
- ==GAS-GCN==
    - Skeleton-based recognition depends on the contexts and long-range dependencies in temporal dimension
    - 但不是所有資訊都會用到action recognition上
    - solution: Gated mechanism
        - control the information flowing in RNN
        - operates the time dimension
        - 


## Source code
- [Tian Lab](https://github.com/Tian-lab/gas-gcn/tree/master)
- [STA-GCN](https://www.semanticscholar.org/paper/Spatial-Temporal-Attention-Graph-Convolutional-with-Shiraki-Hirakawa/4280ab3111c6d4922a832f11f7a073afe062736d)
- [STA-GCN paper](https://openaccess.thecvf.com/content/ACCV2020/papers/Shiraki_Spatial_Temporal_Attention_Graph_Convolutional_Networks_with_Mechanics-Stream_for_Skeleton-based_ACCV_2020_paper.pdf)
- 
