# Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval

arXiv: https://arxiv.org/abs/2503.19296 · HTML: https://mlsanity.com/paper/2503.19296/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [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)
- [Zero-Shot Composed Image Retrieval with Textual Inversion](https://mlsanity.com/paper/2303.15247/) (2023)
- [Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2302.03084/) (2023)
- ["This is my unicorn, Fluffy": Personalizing frozen vision-language representations](https://mlsanity.com/paper/2204.01694/) (2022)
- [ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity](https://mlsanity.com/paper/2203.08101/) (2022)
- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [Compositional Learning of Image-Text Query for Image Retrieval](https://mlsanity.com/paper/2006.11149/) (2020)
- [FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks](https://mlsanity.com/paper/2303.02483/) (2023)
- [Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback](https://mlsanity.com/paper/2106.04128/) (2021)
- [Progressive Learning for Image Retrieval with Hybrid-Modality Queries](https://mlsanity.com/paper/2204.11212/) (2022)
- [Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval](https://mlsanity.com/paper/2305.09979/) (2023)

## Built on by (most cited first, 18 of 18)

- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2505.19952/) (2025)
- [MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2505.19707/) (2025)
- [From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2504.17990/) (2025)
- [Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning](https://mlsanity.com/paper/2601.11393/) (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)
- [FACap: A Large-scale Fashion Dataset for Fine-grained Composed Image Retrieval](https://mlsanity.com/paper/2507.07135/) (2025)
- [Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration](https://mlsanity.com/paper/2601.14060/) (2026)
- [OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval](https://mlsanity.com/paper/2602.08603/) (2026)
- [FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval](https://mlsanity.com/paper/2607.27959/) (2026)
- [Learning Sample-wise Rank-aware Interpolation Weights for Composed Visual Data Retrieval](https://mlsanity.com/paper/2608.22500/) (2026)
- [Pix2Key: Controllable Open-Vocabulary Retrieval with Semantic Decomposition and Self-Supervised Visual Dictionary Learning](https://mlsanity.com/paper/2602.22510/) (2026)
- [AlbumFill: Album-Guided Reasoning and Retrieval for Personalized Image Completion](https://mlsanity.com/paper/2605.02892/) (2026)
- [Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism](https://mlsanity.com/paper/2606.31222/) (2026)
- [DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2607.04665/) (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) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-shot CIR (ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer ZS-CIR studies focus on converting the CIR task into a standard text-to-image retrieval task by pre-training a textual inversion network that can map a given image into a single pseudo-word token. Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained pseudo-word token mapping and tri-wise caption-based semantic regularization. The former maps the image into a subject-oriented pseudo-word token and several attribute-oriented pseudo-word tokens to comprehensively express the image in the textual form, while the latter works on jointly aligning the fine-grained pseudo-word tokens to the real-word token embedding space based on a BLIP-generated image caption template. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed method.