3 accepted papers
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in general visual understanding, they suffer from a fundamental geometric fragility: standard visual representations often degrade rapidly under changes in viewpoint and viewing distance. Our analysis identifies that ex…
High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods struggle with the trade-off between coverage and efficiency. Visual expert-assisted search is efficient but prone to blind s…
While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoning to visual perception. Current methods, such as latent query alignment, are end-to-end yet opaque "black boxes". Conversely, textual localization