3 accepted papers
Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understanding. However, existing benchmarks typically evaluate agents with fully synthetic, single-turn queries, limited visual modalities, and lack a framework to asses…
Medical imaging remains challenging due to acoustic shadows, motion blur, and indistinct boundaries, while adapting vision models to such domains often requires heavy task-specific fine-tuning and can degrade general-image capability. We propose DCRM-ViT, a domain-conditioned residual modulation fra
Capitalizing on a vast amount of image-text data, large-scale vision-language pre-training has demonstrated remarkable zero-shot capabilities and has been utilized in several applications. However, models trained on general everyday web-crawled data often exhibit sub-optimal performance for speciali…