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

• URL: http://arxiv.org/abs/2605.08389v1
• Date: Fri, 08 May 2026 18:55:41 GMT
• Title: Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval
• Abstract summary: We show that DeCIR consistently improves projection-based ZS-CIR without increasing inference complexity.<n>Experiments on CIRR, CIRCO, FashionIQ, and GeneCIS demonstrate that DeCIR consistently improves projection-based ZS-CIR without increasing inference complexity.
• Score: 61.75093083322011
• License: http://creativecommons.org/licenses/by/4.0/
• 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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