Toward Detail-Intensive E-commerce Image-Text Retrieval: Hierarchical Refinement of Query-Relevant Representations

E-commerce image-text retrieval (E-ITR) aims to rapidly retrieve relevant products from large-scale image corpora in response to user textual queries, supporting everyday shopping search, recommendation, and assistance. As living standards improve and product choices expand, E-commerce images become increasingly detail-intensive, containing densely packed elements such as product logos and usage instructions. Moreover, real-time response requirements necessitate retrieval from low-resolution E-commerce images, making it more challenging to accurately identify query-relevant products. To address the dilemma, we contribute EComClutter, a detail-intensive E-commerce image-text retrieval database with 94,481 query-image pairs, which are sourced from 35 E-commerce categories (e.g., fashion and electronics). Relevance is annotated through an ensemble of chain-of-thought reasoning outputs from multiple advanced MLLMs, followed by rigorous human verification. Based on EComClutter, we further propose EHiRe, a novel E-ITR framework that hierarchically refines query-relevant representations through Detail Region Rearrangement, Important Tokens Modulation, and Rel- evant Embedding Selection. EHiRe achieves state-of-the-art retrieval accuracy under low-resolution inference settings. Extensive experiments and comparisons demonstrate the superiority of the proposed method.