arXiv:2606.01612v1 Announce Type: cross Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning? In this work, we provide an affirmative answer. Attention structure in LVLMs encodes grounding quality-a lightweight IoU regressor trained solely on attention maps achieves strong IoU prediction (Pearson r > 0.67). This regressor powers the regressor-based variant of our Attention-based Candidate Selection (ACS) framework, called ACS-Learned, which selects the best box from multiple sampled candidates to improve obje
The rapid advancement and widespread deployment of Large Vision Language Models (LVLMs) are driving research into improving their object recognition capabilities, particularly for small objects.
Improved small object grounding in LVLMs enhances the reliability and precision of AI systems, expanding their utility in critical applications requiring granular visual understanding.
LVLMs can now identify small objects with greater accuracy without extensive fine-tuning, potentially accelerating the development and deployment of more sophisticated AI vision systems.
- · AI developers
- · Computer Vision sector
- · Robotics
- · Surveillance systems
AI systems will become more adept at tasks requiring precise recognition of minute details in complex visual environments.
This capability could lead to more robust autonomous systems, quality control in manufacturing, and advanced medical imaging analysis.
Wider adoption could further fuel demand for computational resources and specialized hardware, impacting the compute supply chain.
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Read at arXiv cs.LG