1 citations · 1 across the 8 of their papers we have counts for
8 papers
Taming Visual Neglect: A Variational Information Bottleneck Framework for Adaptive Attention in Multimodal In-Context Learning
Kaito Tanaka, Yuji Nishimura, Keisuke Matsuda +1
Large vision-language models exhibit strong in-context learning (ICL) capabilities, yet when and why visual context helps multimodal ICL remains poorly understood. Empirical studie…
AE-LLM: Adaptive Efficiency Optimization for Large Language Models
Kaito Tanaka, Masato Ito, Yuji Nishimura +2
Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory r…
Contextual Discrepancy-Aware Contrastive Learning for Robust Medical Time Series Diagnosis in Small-Sample Scenarios
Kaito Tanaka, Aya Nakayama, Masato Ito +2
Medical time series data, such as EEG and ECG, are vital for diagnosing neurological and cardiovascular diseases. However, their precise interpretation faces significant challenges…
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…
High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers
Brian Wong, Kaito Tanaka
Automated labeling of chest X-ray reports is essential for enabling downstream tasks such as training image-based diagnostic models, population health studies, and clinical decisio…
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…
Leveraging Language Models for Emotion and Behavior Analysis in Education
Kaito Tanaka, Benjamin Tan, Brian Wong
The analysis of students' emotions and behaviors is crucial for enhancing learning outcomes and personalizing educational experiences. Traditional methods often rely on intrusive v…