arXiv:2607.00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) alleviates this reliance through proxy tasks trained on image-text pairs. However, existing proxy tasks primarily enhance visual and textual representations to accommodate a predefined composition mechanism such as pseudo-word injection into a frozen text encoder or linear feature arithmetic. As a result, the composition function itself remains unlearned, limiting the m
The continuous evolution of AI research pushes for more efficient and less data-intensive methods in specialized AI tasks like image retrieval. This paper addresses current limitations in Zero-Shot Composed Image Retrieval (ZS-CIR) by rethinking proxy task design.
Improving ZS-CIR reduces reliance on costly, human-annotated datasets, accelerating AI development and deployment in visual search applications. More effective zero-shot learning could democratize advanced AI capabilities by lowering resource barriers.
Current methods for ZS-CIR, which rely on rigid, unlearned composition functions, are being challenged by new approaches that aim to learn the composition function itself. This could lead to more robust and generalized image retrieval systems.
- · AI researchers
- · Developers of visual search engines
- · E-commerce platforms
- · Content management systems
- · Providers of large, annotated visual datasets
Zero-shot composed image retrieval becomes more accurate and efficient.
Reduced need for extensive human data-labeling for specific visual search tasks, lowering development costs and accelerating innovation.
The broader application of AI in visual content analysis could expand beyond current limits, influencing areas like digital asset management, media forensics, and augmented reality.
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Read at arXiv cs.CL