FreestyleRet: Retrieving Images from Style-Diversified Queries

Hao Li, Yanhao Jia, Peng Jin, Zesen Cheng, Kehan Li, Jialu Sui, Chang Liu, Li Yuan

Abstract: Image Retrieval aims to retrieve corresponding images based on a given query. In application scenarios, users intend to express their retrieval intent through various query styles. However, current retrieval tasks predominantly focus on text-query retrieval exploration, leading to limited retrieval query options and potential ambiguity or bias in user intention. In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, which enables retrieval based on various query styles. To facilitate the novel setting, we propose the first Diverse-Style Retrieval dataset, encompassing diverse query styles including text, sketch, low-resolution, and art. We also propose a light-weighted style-diversified retrieval framework. For various query style inputs, we apply the Gram Matrix to extract the query’s textural features and cluster them into a style space with style-specific bases. Then we employ the style-init prompt learning module to enable the visual encoder to comprehend the texture and style information of the query. Experiments demonstrate that our model outperforms existing retrieval models on the style-diversified retrieval task. Moreover, style-diversified queries (sketch+text, art+text, etc.) can be simultaneously retrieved in our model. The auxiliary information from other queries enhances the performance within the respective query, which may hold potential significance for the community.