DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2607.04665v1
• Date: Mon, 06 Jul 2026 04:37:30 GMT
• Title: DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
• Abstract summary: Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text.<n>We propose DiCE-CIR, a direct composition learning method that predicts composed query representations by directly composing a reference image and an edit text.
• Score: 52.15696141238419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word tokens in the text embedding space. However, such methods require additional projection and re-encoding steps, increasing training complexity, reducing efficiency, and introducing a discrepancy between training and inference. In this paper, we propose DiCE-CIR, a direct composition learning method that predicts composed query representations by directly composing a reference image and an edit text. To enable scalable training without manually annotated triplets, we automatically construct compositional training samples from large-scale image-caption pairs using a large language model. Based on these samples, we train a lightweight composition module with objectives that promote alignment with the target, edit-consistent semantic transformation, and retrieval discriminability. We conduct extensive experiments on ZS-CIR benchmarks and show that DiCE-CIR achieves state-of-the-art performance on CIRCO and competitive performance on CIRR while maintaining high computational efficiency.

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