1. Graph-Based Global
Reasoning Networks
Yunpeng Chen, Marcus Rohrbach , Zhicheng Yan , Shuicheng Yan, Jiashi
Feng , Yannis Kalantidis
IVUL Group Meeting
Aug 6
Present by Frost
CVPR 2019

2. Motivation

3. Related Design: Non Local and Double Attention
The Double Attention Block

4. Related Design: Squeeze-and-Excitation Networks

5. •
• W denotes parameters of the convolution
•
•
𝑌 = 𝐴 𝑋 𝑊
kernels d*d
A is the adjacent Matrix n*n
X is the node feature n*d
• Torch.enisum(‘xnn,xnd,dd->xnd’)
CNN Method

6. Step 1 Coordinate Space to Interaction Space

7. Step 2 Graph Convolution
adjacency matrix Ag:
randomly initialized and learned
first step: Laplacian smoothing

8. Step 3 Back to Coordinate Space
Similar as step 1

9. Overview

10. Object Detection

11. Semantic Segmentation && Action Recognition

12. Discussion
1. Feature of all the pixels v.s. convolution on locations ?
2. Fully connected graph v.s. convolution on locations ?
= 𝑉 𝑇 𝑊𝑎
𝑇 𝑊𝐺
𝑉 = 𝐵 𝑋 = 𝑋 𝑇 𝑊𝑏
𝑇
Really?