[Paper note] Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

• Author: Rahul Rama Varior, NTU Singapore; Mrinal Haloi, Nanyang technological Universi; Gang Wang
• Picutre of the model

• Current state-of-the-art on Market-1501.

Contribution

1. Architecture of baseline siamese network for person re-id.
2. Matching gate between convolutional blocks.

Matching gate (MG) structure

• Feature summarization
  
  • Aggregates the local feature along a horizontal stripe.
  • Deal with problem of changed view point (view point change in re-id typically in the horizontal direction, same parts are very likely to be along the same horizontal region).
  • Equation and dimention:
    ;
    where is the row of feature map. (maybe ) and (maybe ).
• Feature similarity
  
  • Euclidean distance of and
    
    where
  • decides the variance of the Gaussian function, learnable, should set a higher initial value.
• Filtering and boosting the features
  
  • Repeat c times horizontally to obtain .
  • Add filtered feature to original feature.
    
    
  • Perform L2 normalization across channels after this

Result

• Dataset: Market-1501, CUHK-03, VIPeR.
• Baseline S-CNN outperform most CNN approaches. With MG gaining further improvement.
• Visualization of gate. Low gate activation means low similarity.