Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID

TL;DR

This paper introduces a scalable, transferable text-to-image person re-identification approach using large-scale multi-modal language models, addressing description diversity and accuracy issues to improve cross-dataset performance.

Contribution

The study proposes novel methods for generating diverse textual descriptions and automatically filtering incorrect words, enhancing transferability and performance of text-to-image ReID models.

Findings

Significant improvement in cross-dataset ReID accuracy.

State-of-the-art results in traditional evaluation settings.

Effective handling of noisy textual descriptions.

Abstract

Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming, restricting the scale of existing datasets and therefore the generalization ability of ReID models. As a result, we study the transferable text-to-image ReID problem, where we train a model on our proposed large-scale database and directly deploy it to various datasets for evaluation. We obtain substantial training data via Multi-modal Large Language Models (MLLMs). Moreover, we identify and address two key challenges in utilizing the obtained textual descriptions. First, an MLLM tends to generate descriptions with similar structures, causing the model to overfit specific sentence patterns. Thus, we propose a novel method that uses MLLMs to caption images according to various templates. These templates are obtained using a multi-turn dialogue with a Large Language Model (LLM). Therefore, we can build a large-scale dataset with diverse textual descriptions. Second, an MLLM may produce incorrect descriptions. Hence, we introduce a novel method that automatically identifies words in a description that do not correspond with the image. This method is based on the similarity between one text and all patch token embeddings in the image. Then, we mask these words with a larger probability in the subsequent training epoch, alleviating the impact of noisy textual descriptions. The experimental results demonstrate that our methods significantly boost the direct transfer text-to-image ReID performance. Benefiting from the pre-trained model weights, we also achieve state-of-the-art performance in the traditional evaluation settings.