Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification

TL;DR

This paper introduces CBDB-Net, a person Re-ID model with novel modules and loss function that improve robustness and accuracy by generating incomplete features and balancing sample difficulty during training.

Contribution

The paper proposes the CBDB-Net with Consecutive Batch DropBlock Module and Elastic Loss, which enhance person re-identification by capturing robust descriptors and adaptively balancing training samples.

Findings

Achieves competitive performance on standard person Re-ID datasets.

Improves robustness in occluded person Re-ID scenarios.

Demonstrates effectiveness of novel modules through ablation studies.

Abstract

In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).