# Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval

arXiv: https://arxiv.org/abs/2412.11077 · HTML: https://mlsanity.com/paper/2412.11077/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 59 of its 74 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 30 of 59)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [A Corpus for Reasoning About Natural Language Grounded in Photographs](https://mlsanity.com/paper/1811.00491/) (2018)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models](https://mlsanity.com/paper/2108.04024/) (2021)
- [Composing Text and Image for Image Retrieval - An Empirical Odyssey](https://mlsanity.com/paper/1812.07119/) (2018)
- [Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation](https://mlsanity.com/paper/2003.08813/) (2020)
- [Zero-Shot Composed Image Retrieval with Textual Inversion](https://mlsanity.com/paper/2303.15247/) (2023)
- [Compositional Chain-of-Thought Prompting for Large Multimodal Models](https://mlsanity.com/paper/2311.17076/) (2023)

## Built on by (most cited first, 30 of 32)

- [Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2503.17109/) (2025)
- [A Comprehensive Survey on Composed Image Retrieval](https://mlsanity.com/paper/2502.18495/) (2025)
- [CoVR-R:Reason-Aware Composed Video Retrieval](https://mlsanity.com/paper/2603.20190/) (2026)
- [TRACE: Task-Adaptive Reasoning and Representation Learning for Universal Multimodal Retrieval](https://mlsanity.com/paper/2603.02929/) (2026)
- [Retrieving Counterfactuals Improves Visual In-Context Learning](https://mlsanity.com/paper/2603.16737/) (2026)
- [WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2602.23029/) (2026)
- [Imagine Before Concentration: Diffusion-Guided Registers Enhance Partially Relevant Video Retrieval](https://mlsanity.com/paper/2604.03653/) (2026)
- [From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2504.17990/) (2025)
- [MCoT-RE: Multi-Faceted Chain-of-Thought and Re-Ranking for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2507.12819/) (2025)
- [TATTOO: Training-free AesTheTic-aware Outfit recOmmendation](https://mlsanity.com/paper/2509.23242/) (2025)
- [PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval](https://mlsanity.com/paper/2603.01493/) (2026)
- [G-MIXER: Geodesic Mixup-based Implicit Semantic Expansion and Explicit Semantic Re-ranking for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2604.14710/) (2026)
- [UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval](https://mlsanity.com/paper/2604.20318/) (2026)
- [STiTch: Semantic Transition and Transportation in Collaboration for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2605.21261/) (2026)
- [SETR: A Two-Stage Semantic-Enhanced Framework for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2509.26012/) (2025)
- [SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2509.26330/) (2025)
- [REVISION:Reflective Intent Mining and Online Reasoning Auxiliary for E-commerce Visual Search System Optimization](https://mlsanity.com/paper/2510.22739/) (2025)
- [X-Aligner: Composed Visual Retrieval without the Bells and Whistles](https://mlsanity.com/paper/2601.16582/) (2026)
- [Generating a Paracosm for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2602.00813/) (2026)
- [OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval](https://mlsanity.com/paper/2602.08603/) (2026)
- [Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets](https://mlsanity.com/paper/2606.07032/) (2026)
- [Learning Sample-wise Rank-aware Interpolation Weights for Composed Visual Data Retrieval](https://mlsanity.com/paper/2608.22500/) (2026)
- [Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering](https://mlsanity.com/paper/2508.11272/) (2025)
- [Generative Editing in the Joint Vision-Language Space for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2512.01636/) (2025)
- [A Sanity Check on Composed Image Retrieval](https://mlsanity.com/paper/2604.12904/) (2026)
- [AlbumFill: Album-Guided Reasoning and Retrieval for Personalized Image Completion](https://mlsanity.com/paper/2605.02892/) (2026)
- [Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2605.08389/) (2026)
- [Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction](https://mlsanity.com/paper/2605.24634/) (2026)
- [Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism](https://mlsanity.com/paper/2606.31222/) (2026)
- [MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2608.25305/) (2026)

## Abstract (arXiv)

Composed Image Retrieval (CIR) aims to retrieve target images that closely resemble a reference image while integrating user-specified textual modifications, thereby capturing user intent more precisely. Existing training-free zero-shot CIR (ZS-CIR) methods often employ a two-stage process: they first generate a caption for the reference image and then use Large Language Models for reasoning to obtain a target description. However, these methods suffer from missing critical visual details and limited reasoning capabilities, leading to suboptimal retrieval performance. To address these challenges, we propose a novel, training-free one-stage method, One-Stage Reflective Chain-of-Thought Reasoning for ZS-CIR (OSrCIR), which employs Multimodal Large Language Models to retain essential visual information in a single-stage reasoning process, eliminating the information loss seen in two-stage methods. Our Reflective Chain-of-Thought framework further improves interpretative accuracy by aligning manipulation intent with contextual cues from reference images. OSrCIR achieves performance gains of 1.80% to 6.44% over existing training-free methods across multiple tasks, setting new state-of-the-art results in ZS-CIR and enhancing its utility in vision-language applications. Our code will be available at https://github.com/Pter61/osrcir2024/.