From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2504.17990v1
• Date: Fri, 25 Apr 2025 00:18:23 GMT
• Title: From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval
• Authors: Yabing Wang, Zhuotao Tian, Qingpei Guo, Zheng Qin, Sanping Zhou, Ming Yang, Le Wang,
• Abstract summary: Composed Image Retrieval (CIR) is a challenging multimodal task that retrieves a target image based on a reference image and accompanying modification text.<n>We propose a two-stage framework where the training is accomplished from mapping to composing.<n>In the first stage, we enhance image-to-pseudo-word token learning by introducing a visual semantic injection module.<n>In the second stage, we optimize the text encoder using a small amount of synthetic triplet data, enabling it to effectively extract compositional semantics.
• Score: 30.33315985826623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Composed Image Retrieval (CIR) is a challenging multimodal task that retrieves a target image based on a reference image and accompanying modification text. Due to the high cost of annotating CIR triplet datasets, zero-shot (ZS) CIR has gained traction as a promising alternative. Existing studies mainly focus on projection-based methods, which map an image to a single pseudo-word token. However, these methods face three critical challenges: (1) insufficient pseudo-word token representation capacity, (2) discrepancies between training and inference phases, and (3) reliance on large-scale synthetic data. To address these issues, we propose a two-stage framework where the training is accomplished from mapping to composing. In the first stage, we enhance image-to-pseudo-word token learning by introducing a visual semantic injection module and a soft text alignment objective, enabling the token to capture richer and fine-grained image information. In the second stage, we optimize the text encoder using a small amount of synthetic triplet data, enabling it to effectively extract compositional semantics by combining pseudo-word tokens with modification text for accurate target image retrieval. The strong visual-to-pseudo mapping established in the first stage provides a solid foundation for the second stage, making our approach compatible with both high- and low-quality synthetic data, and capable of achieving significant performance gains with only a small amount of synthetic data. Extensive experiments were conducted on three public datasets, achieving superior performance compared to existing approaches.

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