Transferring a Semantic Representation for Person Re-Identification and Search

TL;DR

This paper introduces a transferable semantic attribute learning method for person re-identification and search, trained on fashion datasets, enabling effective application to surveillance data without domain-specific annotations.

Contribution

The proposed approach allows transfer of semantic attributes from fashion datasets to surveillance, improving re-identification and search without extensive domain-specific labeling.

Findings

Achieves state-of-the-art performance in supervised re-identification

Near state-of-the-art in unsupervised re-identification

Enables integrated description-based person search

Abstract

Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their non-scalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets -- either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.