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

arXiv: https://arxiv.org/abs/2412.13834 · HTML: https://mlsanity.com/paper/2412.13834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training](https://mlsanity.com/paper/2303.03032/) (2023)
- [Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders](https://mlsanity.com/paper/2008.05231/) (2020)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets](https://mlsanity.com/paper/2007.06877/) (2020)
- [Context-Aware Group Captioning via Self-Attention and Contrastive Features](https://mlsanity.com/paper/2004.03708/) (2020)
- [Group-based Distinctive Image Captioning with Memory Attention](https://mlsanity.com/paper/2108.09151/) (2021)
- [On Distinctive Image Captioning via Comparing and Reweighting](https://mlsanity.com/paper/2204.03938/) (2022)
- [ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval](https://mlsanity.com/paper/2207.14757/) (2022)
- [Generating Query Recommendations via LLMs](https://mlsanity.com/paper/2405.19749/) (2024)
- [Multi-Image Summarization: Textual Summary from a Set of Cohesive Images](https://mlsanity.com/paper/2006.08686/) (2020)
- [High Quality Related Search Query Suggestions using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.04452/) (2021)

## Built on by (most cited first, 1 of 1)

- [Seeing Through Words: Controlling Visual Retrieval Quality with Language Models](https://mlsanity.com/paper/2602.21175/) (2026)

## Abstract (arXiv)

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/