Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification

TL;DR

This paper introduces a camera-invariant meta-learning network for single-camera-training person re-identification, enabling robust feature learning without cross-camera supervision and outperforming existing methods.

Contribution

The proposed CIMN method employs meta-learning with cross-camera simulation and novel loss functions to learn camera-invariant features without CCSP data.

Findings

Achieves comparable performance with and without CCSP data.

Outperforms state-of-the-art methods on SCT re-ID benchmarks.

Enhances domain generalization ability.

Abstract

Single-camera-training person re-identification (SCT re-ID) aims to train a re-ID model using SCT datasets where each person appears in only one camera. The main challenge of SCT re-ID is to learn camera-invariant feature representations without cross-camera same-person (CCSP) data as supervision. Previous methods address it by assuming that the most similar person should be found in another camera. However, this assumption is not guaranteed to be correct. In this paper, we propose a Camera-Invariant Meta-Learning Network (CIMN) for SCT re-ID. CIMN assumes that the camera-invariant feature representations should be robust to camera changes. To this end, we split the training data into meta-train set and meta-test set based on camera IDs and perform a cross-camera simulation via meta-learning strategy, aiming to enforce the representations learned from the meta-train set to be robust to the meta-test set. With the cross-camera simulation, CIMN can learn camera-invariant and identity-discriminative representations even there are no CCSP data. However, this simulation also causes the separation of the meta-train set and the meta-test set, which ignores some beneficial relations between them. Thus, we introduce three losses: meta triplet loss, meta classification loss, and meta camera alignment loss, to leverage the ignored relations. The experiment results demonstrate that our method achieves comparable performance with and without CCSP data, and outperforms the state-of-the-art methods on SCT re-ID benchmarks. In addition, it is also effective in improving the domain generalization ability of the model.