Dual Cluster Contrastive learning for Object Re-Identification

TL;DR

This paper introduces a dual cluster contrastive learning framework for object re-identification that combines individual and centroid-based memory updates, improving discriminative feature learning in supervised and unsupervised settings.

Contribution

The paper proposes the Dual Cluster Contrastive (DCC) framework, unifying individual and centroid-based cluster memory updates with a cross-view consistency constraint for enhanced object ReID.

Findings

DCC outperforms existing methods on multiple benchmarks.

Effective in both supervised and unsupervised ReID tasks.

Reduces impact of outliers through centroid-based updates.

Abstract

Recently, cluster contrastive learning has been proven effective for object ReID by computing the contrastive loss between the individual features and the cluster memory. However, existing methods that use the individual features to momentum update the cluster memory will fluctuate over the training examples, especially for the outlier samples. Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory can reduce the impact of individual samples. Therefore, we formulate the individual-based updating and centroid-based updating mechanisms in a unified cluster contrastive framework, named Dual Cluster Contrastive framework (DCC), which maintains two types of memory banks: individual and centroid cluster memory banks. Significantly, the individual cluster memory considers just one individual at a time to take a single step for updating. The centroid cluster memory applies the mean feature of each cluster to update the corresponding cluster memory. During optimization, besides the vallina contrastive loss of each memory, a cross-view consistency constraint is applied to exchange the benefits of two memories for generating a discriminative description for the object ReID. Note that DCC can be easily applied for unsupervised or supervised object ReID by using ground-truth labels or the generated pseudo-labels. Extensive experiments on three benchmarks, \emph{e.g.,} Market-1501, MSMT17, and VeRi-776, under \textbf{supervised Object ReID} and \textbf{unsupervised Object ReID} demonstrate the superiority of the proposed DCC.