FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization
arXiv:2605.31145v1 Announce Type: cross Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly without training or parameter updates. Despite rapid advances in vision-language models (VLMs), achieving category-agnostic and visually grounded ICL remains an open problem, even though it is essential for applications such as image editing, personalized visual search, and retrieval. Existing methods are fragile and rely on explicit category supervision, which not only limits applicability in reali
The paper outlines a novel approach to in-context localization, leveraging visual support constraints and policy optimization, which arrives amidst rapid advancements and increasing deployment of vision-language models.
Achieving category-agnostic and visually grounded in-context localization is critical for advancing practical applications of AI in areas like image editing, personalized search, and retrieval, pushing beyond current VLM limitations.
Current methods for in-context localization are often fragile and tied to explicit category supervision; this work promises a more robust and generalizable approach, independent of predefined categories.
- · AI developers
- · Image editing software companies
- · Personalized visual search platforms
- · Robotics
- · Platforms relying on rigid, category-specific visual AI
More accurate and versatile object localization will enable new human-computer interaction paradigms.
Improved image editing and augmented reality applications will become commonplace, enhancing daily digital experiences.
Enhanced visual understanding could lead to significant advancements in general-purpose AI and autonomous systems, potentially accelerating the development of agentic AI capabilities.
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Read at arXiv cs.LG