Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2505.19952v1
• Date: Mon, 26 May 2025 13:17:50 GMT
• Title: Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
• Authors: Rong-Cheng Tu, Wenhao Sun, Hanzhe You, Yingjie Wang, Jiaxing Huang, Li Shen, Dacheng Tao,
• Abstract summary: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a compositional query.<n>We propose a novel framework by employing a Multimodal Reasoning Agent (MRA) for ZS-CIR.
• Score: 52.709090256954276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a compositional query, consisting of a reference image and a modifying text-without relying on annotated training data. Existing approaches often generate a synthetic target text using large language models (LLMs) to serve as an intermediate anchor between the compositional query and the target image. Models are then trained to align the compositional query with the generated text, and separately align images with their corresponding texts using contrastive learning. However, this reliance on intermediate text introduces error propagation, as inaccuracies in query-to-text and text-to-image mappings accumulate, ultimately degrading retrieval performance. To address these problems, we propose a novel framework by employing a Multimodal Reasoning Agent (MRA) for ZS-CIR. MRA eliminates the dependence on textual intermediaries by directly constructing triplets, <reference image, modification text, target image>, using only unlabeled image data. By training on these synthetic triplets, our model learns to capture the relationships between compositional queries and candidate images directly. Extensive experiments on three standard CIR benchmarks demonstrate the effectiveness of our approach. On the FashionIQ dataset, our method improves Average R@10 by at least 7.5\% over existing baselines; on CIRR, it boosts R@1 by 9.6\%; and on CIRCO, it increases mAP@5 by 9.5\%.

Related papers

• Scaling Prompt Instructed Zero Shot Composed Image Retrieval with Image-Only Data [39.17652541259225]
  Composed Image Retrieval (CIR) is the task of retrieving images matching a reference image augmented with a text.<n>We introduce an embedding reformulation architecture that effectively combines image and text modalities.<n>Our model, named InstructCIR, outperforms state-of-the-art methods in zero-shot composed image retrieval on CIRR and FashionIQ datasets.
  arXiv  Detail & Related papers  (2025-04-01T14:03:46Z)
• CoLLM: A Large Language Model for Composed Image Retrieval [76.29725148964368]
  Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query.<n>We present CoLLM, a one-stop framework that generates triplets on-the-fly from image-caption pairs.<n>We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts.
  arXiv  Detail & Related papers  (2025-03-25T17:59:50Z)
• Data-Efficient Generalization for Zero-shot Composed Image Retrieval [67.46975191141928]
  ZS-CIR aims to retrieve the target image based on a reference image and a text description without requiring in-distribution triplets for training.<n>One prevalent approach follows the vision-language pretraining paradigm that employs a mapping network to transfer the image embedding to a pseudo-word token in the text embedding space.<n>We propose a Data-efficient Generalization (DeG) framework, including two novel designs, namely, Textual Supplement (TS) module and Semantic-Set (S-Set)
  arXiv  Detail & Related papers  (2025-03-07T07:49:31Z)
• Training-free Zero-shot Composed Image Retrieval via Weighted Modality Fusion and Similarity [2.724141845301679]
  Composed image retrieval (CIR) formulates the query as a combination of a reference image and modified text. We introduce a training-free approach for ZS-CIR. Our approach is simple, easy to implement, and its effectiveness is validated through experiments on the FashionIQ and CIRR datasets.
  arXiv  Detail & Related papers  (2024-09-07T21:52:58Z)
• Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval [43.47770490199544]
  Composed Image Retrieval (CIR) is a complex task that retrieves images using a query, which is configured with an image and a caption. We introduce a novel ZS-CIR method that uses Spherical Linear Interpolation (Slerp) to directly merge image and text representations. We also introduce Text-Anchored-Tuning (TAT), a method that fine-tunes the image encoder while keeping the text encoder fixed.
  arXiv  Detail & Related papers  (2024-05-01T15:19:54Z)
• Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval [50.72924579220149]
  Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the reference image, text, target image. We propose a new semi-supervised CIR approach where we search for a reference and its related target images in auxiliary data.
  arXiv  Detail & Related papers  (2024-04-23T21:00:22Z)
• Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking [34.31345844296072]
  Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. Most current composed image retrieval methods follow a supervised learning approach to training on a costly triplet dataset composed of a reference image, modified text, and a corresponding target image. We present a new training-free zero-shot composed image retrieval method which translates the query into explicit human-understandable text.
  arXiv  Detail & Related papers  (2023-12-14T13:31:01Z)
• Zero-shot Composed Text-Image Retrieval [72.43790281036584]
  We consider the problem of composed image retrieval (CIR) It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
  arXiv  Detail & Related papers  (2023-06-12T17:56:01Z)
• Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval [84.11127588805138]
  Composed Image Retrieval (CIR) combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. We propose Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training.
  arXiv  Detail & Related papers  (2023-02-06T19:40:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.