arXiv:2604.11539v2 Announce Type: replace-cross Abstract: Human perception of visual similarity is inherently adaptive and subjective, depending on the users' interests and focus. However, most image retrieval systems fail to reflect this flexibility, relying on a fixed, monolithic metric that cannot incorporate multiple conditions simultaneously. To address this, we propose CLAY, an adaptive similarity computation method that reframes the embedding space of pretrained Vision-Language Models (VLMs) as a text-conditional similarity space without additional training. This design separates the te
The proliferation of Vision-Language Models (VLMs) and the increasing need for adaptive and personalized information retrieval systems necessitate more flexible similarity computation methods.
This development enhances the practical utility and adaptability of AI vision systems, moving beyond fixed metrics to more context-aware and user-centric applications, which is crucial for advanced AI agent development.
Image retrieval and visual similarity tasks can now be dynamically modulated based on textual conditions without additional training, making AI systems more versatile and reflective of human perception.
- · AI developers
- · E-commerce platforms
- · Content creators
- · Autonomous systems
- · Traditional fixed metric retrieval systems
Improved performance and user experience in visual search and content organization.
Accelerated development of more sophisticated AI agents capable of understanding and adapting to nuanced visual queries.
New forms of human-AI collaboration where visual information is processed and adapted to individual preferences, blurring the lines between static data and dynamic interpretation.
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Read at arXiv cs.AI