# Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

arXiv: https://arxiv.org/abs/1905.12794 · HTML: https://mlsanity.com/paper/1905.12794/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 30 of 43)

- [Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models](https://mlsanity.com/paper/2108.04024/) (2021)
- [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://mlsanity.com/paper/2411.02571/) (2024)
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- [GeneCIS: A Benchmark for General Conditional Image Similarity](https://mlsanity.com/paper/2306.07969/) (2023)
- [Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization](https://mlsanity.com/paper/2211.07394/) (2022)
- [Bi-directional Training for Composed Image Retrieval via Text Prompt Learning](https://mlsanity.com/paper/2303.16604/) (2023)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder](https://mlsanity.com/paper/2305.16304/) (2023)
- [Probabilistic Compositional Embeddings for Multimodal Image Retrieval](https://mlsanity.com/paper/2204.05845/) (2022)
- [Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking](https://mlsanity.com/paper/2312.08924/) (2023)
- [CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval](https://mlsanity.com/paper/2405.19149/) (2024)
- [EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval](https://mlsanity.com/paper/2407.16658/) (2024)
- [CoLLM: A Large Language Model for Composed Image Retrieval](https://mlsanity.com/paper/2503.19910/) (2025)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy](https://mlsanity.com/paper/2411.16752/) (2024)
- [Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training](https://mlsanity.com/paper/2108.11119/) (2021)
- [HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels](https://mlsanity.com/paper/2407.05795/) (2024)
- [CoVR-R:Reason-Aware Composed Video Retrieval](https://mlsanity.com/paper/2603.20190/) (2026)
- [Image Search with Text Feedback by Additive Attention Compositional Learning](https://mlsanity.com/paper/2203.03809/) (2022)
- [ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval](https://mlsanity.com/paper/2505.20764/) (2025)
- [Generalized Contrastive Learning for Universal Multimodal Retrieval](https://mlsanity.com/paper/2509.25638/) (2025)
- [Training and challenging models for text-guided fashion image retrieval](https://mlsanity.com/paper/2204.11004/) (2022)
- [Ranking-aware Uncertainty for Text-guided Image Retrieval](https://mlsanity.com/paper/2308.08131/) (2023)
- [Dual Relation Alignment for Composed Image Retrieval](https://mlsanity.com/paper/2309.02169/) (2023)
- [I Want This Product but Different : Multimodal Retrieval with Synthetic Query Expansion](https://mlsanity.com/paper/2102.08871/) (2021)
- [Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing](https://mlsanity.com/paper/2111.12346/) (2021)
- [SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts](https://mlsanity.com/paper/2108.12992/) (2021)
- [Benchmarking Robustness of Text-Image Composed Retrieval](https://mlsanity.com/paper/2311.14837/) (2023)
- [PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval](https://mlsanity.com/paper/2603.01493/) (2026)
- [Cycled Compositional Learning between Images and Text](https://mlsanity.com/paper/2107.11509/) (2021)

## Abstract (arXiv)

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval. We believe that our dataset will encourage further work on developing more natural and real-world applicable conversational shopping assistants.