Stochastic attentions and context learning for person re-identification.

ABSTRACT: The discriminative parts of people's appearance play a significant role in their re-identification across non overlapping camera views. However, just focusing on the discriminative or attention regions without catering the contextual information does not always help. It is more important to learn the attention with reference to their spatial locations in context of the whole image. Current person re-identification (re-id) approaches either use separate modules or classifiers to learn both of these; the attention and its context, resulting in highly expensive person re-id solutions. In this work, instead of handling attentions and the context separately, we employ a unified attention and context mapping (ACM) block within the convolutional layers of network, without any additional computati

SUBMITTER: Perwaiz N 

PROVIDER: S-EPMC8114801 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature