# Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval

arXiv: https://arxiv.org/abs/2605.17365 · HTML: https://mlsanity.com/paper/2605.17365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [Augmenting Language Models with Long-Term Memory](https://mlsanity.com/paper/2306.07174/) (2023)
- [Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders](https://mlsanity.com/paper/2008.05231/) (2020)
- [Chatting Makes Perfect: Chat-based Image Retrieval](https://mlsanity.com/paper/2305.20062/) (2023)
- [Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach](https://mlsanity.com/paper/2406.03411/) (2024)
- [HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing](https://mlsanity.com/paper/2405.06067/) (2024)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning](https://mlsanity.com/paper/2503.10166/) (2025)
- [Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning](https://mlsanity.com/paper/2602.07605/) (2026)
- [Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training](https://mlsanity.com/paper/2306.08789/) (2023)
- [Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering](https://mlsanity.com/paper/2410.09380/) (2024)
- [Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation](https://mlsanity.com/paper/2412.10761/) (2024)
- [Diffusion Augmented Retrieval: A Training-Free Approach to Interactive Text-to-Image Retrieval](https://mlsanity.com/paper/2501.15379/) (2025)
- [GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning](https://mlsanity.com/paper/2410.15266/) (2024)
- [STaR: Sensitive Trajectory Regulation for Unlearning in Large Reasoning Models](https://mlsanity.com/paper/2601.09281/) (2026)

## Abstract (arXiv)

Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refine user intent through multi-round dialogue, thereby achieving more fine-grained retrieval results. The key challenge in this task lies in dynamically understanding and updating the user's query intent across dialogue rounds. Although existing works have achieved great performance on this new task, they simply handle history query information either by directly concatenating all previous queries into a long textual sequence or by relying on large language models to reconstruct the current query from history. Such strategies are computationally redundant and easily lead to inconsistent intent representations as the dialogue progresses. To alleviate these issues, this paper proposes a novel and efficient memory-based user intent updating framework for the chat-based image retrieval task, called Memory-Augmented Query Intent Understanding (MAQIU). It introduces a lightweight memorization module that dynamically aggregates and evolves the semantic representation of query intent across dialogues, while a memory recall mechanism is further employed to prevent intent forgetting and enhance long-term semantic integrity. In addition, MAQIU also integrates historical image retrieval results as visual guidance, allowing the model to strengthen cross-round correlations and refine current visual understanding. Extensive experiments demonstrate that MAQIU achieves substantial performance gains while maintaining high computational efficiency, reducing dialogue encoding FLOPs by 86.4% compared with the prior baseline ChatIR. Source code is available at https://github.com/HuiGuanLab/MAQIU.