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<p>In content-based image retrieval (CBIR), aligning user queries with database images is challenging, especially when the queries are ‘impossible queries’ i.e. 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, while preserving the user’s original intent. We have found limitations in current encoding systems, particularly the limitations of CLIP embeddings. Due to CLIP’s contrastive learning-based training mechanism, it may struggle to identify similarities. To address this challenge, this paper proposes a embedding remapping network for the CLIP encoder and a QRNE baseline algorithm leveraging the remapping network. Experiments show that the remapped embeddings are able to overcome the limitations of CLIP embeddings.</p>

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Settle for the next best option: improving similarity identification of CLIP for impossible text-image retrieval

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Yidan Zhang,
  • Naoto Naka,
  • Shin’ichi Satoh

摘要

In content-based image retrieval (CBIR), aligning user queries with database images is challenging, especially when the queries are ‘impossible queries’ i.e. 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, while preserving the user’s original intent. We have found limitations in current encoding systems, particularly the limitations of CLIP embeddings. Due to CLIP’s contrastive learning-based training mechanism, it may struggle to identify similarities. To address this challenge, this paper proposes a embedding remapping network for the CLIP encoder and a QRNE baseline algorithm leveraging the remapping network. Experiments show that the remapped embeddings are able to overcome the limitations of CLIP embeddings.