31 citations · 31 across the 6 of their papers we have counts for
6 papers
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…
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…
An Application-Agnostic Automatic Target Recognition System Using Vision Language Models
Anthony Palladino, Dana Gajewski, Abigail Aronica +8
We present a novel Automatic Target Recognition (ATR) system using open-vocabulary object detection and classification models. A primary advantage of this approach is that target c…
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…
FOLIO: Natural Language Reasoning with First-Order Logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao +32
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the…