Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
This work addresses the problem of effectively matching multimodal data for person retrieval, which is incremental as it builds on prior methods by improving alignment without explicit part alignments.
The paper tackles the challenge of aligning visual and textual modalities for text-to-image person retrieval by introducing IRRA, a framework that learns implicit relations between local tokens and enhances global matching without prior supervision, achieving state-of-the-art results with Rank-1 accuracy improvements of 3%-9% on three public datasets.
Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods.