1 citations · 1 across the 8 of their papers we have counts for
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Semantic-Preserving Cross-Style Visual Reasoning for Robust Multi-Modal Understanding in Large Vision-Language Models
Aya Nakayama, Brian Wong, Yuji Nishimura +1
The "style trap" poses a significant challenge for Large Vision-Language Models (LVLMs), hindering robust semantic understanding across diverse visual styles, especially in in-cont…
XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis
Masato Ito, Kaito Tanaka, Keisuke Matsuda +1
Diabetic Retinopathy (DR) is a major cause of global blindness, necessitating early and accurate diagnosis. While deep learning models have shown promise in DR detection, their bla…
Optimizing Vision-Language Interactions Through Decoder-Only Models
Kaito Tanaka, Benjamin Tan, Brian Wong
Vision-Language Models (VLMs) have emerged as key enablers for multimodal tasks, but their reliance on separate visual encoders introduces challenges in efficiency, scalability, an…