[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

Architecture of baseline siamese network for person re-id.

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.