4 papers
360° Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method
Huyen T. T. Tran, Van-Quang Nguyen, Farros Alferro +2
Multimodal Large Language Models (MLLMs) have shown impressive abilities in understanding and reasoning over conventional images. However, their perception of 360° images remains l…
CoReTab: Improving Multimodal Table Understanding with Code-driven Reasoning
Van-Quang Nguyen, Takayuki Okatani
Existing datasets for multimodal table understanding, such as MMTab, primarily provide short factual answers without explicit multi-step reasoning supervision. Models trained on th…
TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/Videos
Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma +4
The application of Multi-modal Large Language Models (MLLMs) in Autonomous Driving (AD) faces significant challenges due to their limited training on traffic-specific data and the…
Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction
Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma +3
This paper addresses the problem of predicting hazards that drivers may encounter while driving a car. We formulate it as a task of anticipating impending accidents using a single…