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arXiv · 2605.08389

Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval

Mingyu Liu·Sihan Huang·Yijia Fan·Yinlin Yan·Quan Zhang·Jian-Fang Hu·Jianhuang Lai
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Abstract

Zero-shot composed image retrieval (ZS-CIR) retrieves a target image from a reference image and a text modification without human-annotated CIR triplets. Projection-based ZS-CIR methods are attractive because they do not rely on LLMs at inference and remain lightweight, but they often underperform LLM-based approaches on complex semantic modifications. This gap reflects a semantic transition bottleneck in projection-based ZS-CIR: endpoint-level matching can let the edit text act as a target-side attribute cue rather than grounding it as a source-conditioned semantic transition. We further show that adding semantic transition supervision to the same text adapter creates an endpoint--transition conflict between endpoint alignment and semantic transition alignment. To address this conflict, DeCIR decouples endpoint and transition learning. It constructs paired forward/reverse edit tuples from image-caption pairs, trains separate low-rank text adapter branches for endpoint alignment and semantic transition alignment, and merges them with Low-Rank Directional Merge (LRDM) into one deployable adapter. Extensive experiments on CIRR, CIRCO, FashionIQ, and GeneCIS demonstrate that DeCIR consistently improves projection-based ZS-CIR without increasing inference complexity.

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Mingyu Liu, Sihan Huang, Yijia Fan, Yinlin Yan, Quan Zhang, Jian-Fang Hu, Jianhuang Lai. 2026-05-08. Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval. https://arxiv.org/abs/2605.08389

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