Generative Editing in the Joint Vision-Language Space for Zero-Shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2512.01636v1
• Date: Mon, 01 Dec 2025 13:04:55 GMT
• Title: Generative Editing in the Joint Vision-Language Space for Zero-Shot Composed Image Retrieval
• Authors: Xin Wang, Haipeng Zhang, Mang Li, Zhaohui Xia, Yueguo Chen, Yu Zhang, Chunyu Wei,
• Abstract summary: Composed Image Retrieval (CIR) enables fine-grained visual search by combining a reference image with a textual modification.<n>We propose Fusion-Diff, a novel generative editing framework with high effectiveness and data efficiency designed for multimodal alignment.
• Score: 11.724675700368316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Composed Image Retrieval (CIR) enables fine-grained visual search by combining a reference image with a textual modification. While supervised CIR methods achieve high accuracy, their reliance on costly triplet annotations motivates zero-shot solutions. The core challenge in zero-shot CIR (ZS-CIR) stems from a fundamental dilemma: existing text-centric or diffusion-based approaches struggle to effectively bridge the vision-language modality gap. To address this, we propose Fusion-Diff, a novel generative editing framework with high effectiveness and data efficiency designed for multimodal alignment. First, it introduces a multimodal fusion feature editing strategy within a joint vision-language (VL) space, substantially narrowing the modality gap. Second, to maximize data efficiency, the framework incorporates a lightweight Control-Adapter, enabling state-of-the-art performance through fine-tuning on only a limited-scale synthetic dataset of 200K samples. Extensive experiments on standard CIR benchmarks (CIRR, FashionIQ, and CIRCO) demonstrate that Fusion-Diff significantly outperforms prior zero-shot approaches. We further enhance the interpretability of our model by visualizing the fused multimodal representations.

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