Dynamic Attention Vision-Language Transformer Network for Person Re-identification

Abstract

Multimodal based person re-identification (ReID) has garnered increasing attention in recent years. However, the integration of visual and textual information encounters significant challenges. Biases in feature integration are frequently observed in existing methods, resulting in suboptimal performance and restricted generalization across a spectrum of ReID tasks. At the same time, since there is a domain gap between the datasets used by the pretraining model and the ReID datasets, it has a certain impact on the performance. In response to these challenges, we proposed a dynamic attention vision-language transformer network for the ReID task. In this network, a novel image-text dynamic attention module (ITDA) is designed to promote unbiased feature integration by dynamically assigning the importance of image and text representations. Additionally, an adapter module is adopted to address the domain gap between pretraining datasets and ReID datasets. Our network can capture complex connections between visual and textual information and achieve satisfactory performance. We conducted numerous experiments on ReID benchmarks to demonstrate the efficacy of our proposed method. The experimental results show that our method achieves state-of-the-art performance, surpassing existing integration strategies. These findings underscore the critical role of unbiased feature dynamic integration in enhancing the capabilities of multimodal based ReID models.