Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval

• URL: http://arxiv.org/abs/2412.13834v1
• Date: Wed, 18 Dec 2024 13:24:09 GMT
• Title: Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval
• Authors: Giacomo Pacini, Fabio Carrara, Nicola Messina, Nicola Tonellotto, Giuseppe Amato, Fabrizio Falchi,
• Abstract summary: This work introduces a novel task that focuses on suggesting minimal textual modifications needed to explore visually consistent subsets of the collection. To facilitate the evaluation and development of methods, we present a tailored benchmark named CroQS. Baseline methods from related fields, such as image captioning and content summarization, are adapted for this task to provide reference performance scores.
• Score: 15.757140563856675
• License:
• Abstract: Query suggestion, a technique widely adopted in information retrieval, enhances system interactivity and the browsing experience of document collections. In cross-modal retrieval, many works have focused on retrieving relevant items from natural language queries, while few have explored query suggestion solutions. In this work, we address query suggestion in cross-modal retrieval, introducing a novel task that focuses on suggesting minimal textual modifications needed to explore visually consistent subsets of the collection, following the premise of ''Maybe you are looking for''. To facilitate the evaluation and development of methods, we present a tailored benchmark named CroQS. This dataset comprises initial queries, grouped result sets, and human-defined suggested queries for each group. We establish dedicated metrics to rigorously evaluate the performance of various methods on this task, measuring representativeness, cluster specificity, and similarity of the suggested queries to the original ones. Baseline methods from related fields, such as image captioning and content summarization, are adapted for this task to provide reference performance scores. Although relatively far from human performance, our experiments reveal that both LLM-based and captioning-based methods achieve competitive results on CroQS, improving the recall on cluster specificity by more than 115% and representativeness mAP by more than 52% with respect to the initial query. The dataset, the implementation of the baseline methods and the notebooks containing our experiments are available here: https://paciosoft.com/CroQS-benchmark/

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