BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception
While Multimodal Large Language Models (MLLMs) demonstrate impressive general capabilities, they struggle with fine-grained perception in ultra-high-resolution (UHR) images, particularly for tiny objects in cluttered scenes. Existing methods face a dilemma: they either rely on inefficient prior-free scanning, or depend on static prior-driven heuristics that lack posterior correction to rectify initial model biases. To address this, we propose BVS (Bayesian Visual Search), a framework that formulates perception as a global optimization problem over a continuous spatial-scale manifold. Specifically, BVS bridges prior guidance with posterior correction: it utilizes an early-stop attention ro...
| Authors | Geng Li, Yuxin Peng |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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