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Graph-Based Global Reasoning Networks

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Mengmeng Xu

Yunpeng Chen, Marcus Rohrbach , Zhicheng Yan , Shuicheng Yan, Jiashi Feng , Yannis Kalantidis

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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
Motivation
Related Design: Non Local and Double Attention The Double Attention Block
Related Design: Squeeze-and-Excitation Networks
• • 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
Step 1 Coordinate Space to Interaction Space
Step 2 Graph Convolution adjacency matrix Ag: randomly initialized and learned first step: Laplacian smoothing
Step 3 Back to Coordinate Space Similar as step 1
Overview
Object Detection
Semantic Segmentation && Action Recognition
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?

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Graph-Based Global Reasoning Networks