Query Refinement for Non-Existing Items in Image Retrieval

In content-based image retrieval (CBIR), aligning user queries with database images is challenging, especially when the queries are “impossible queries,“ queries that seek items that are non-existent in the database. These gaps in retrieval systems result in user dissatisfaction. This paper introduces a novel task, query refinement for non-existent items (QRNE), an approach that reformulates impossible queries to better match available content, maintaining the user's original input. We have found limitations in current encoding systems, particularly the limitations of CLIP features. To achieve a QRNE algorithm that can address this challenge, this paper proposes a feature remapping network for the CLIP encoder and a QRNE baseline algorithm leveraging the remapping network. Experiments show that the remapped features are able to overcome the limitations of CLIP features.