In recent years, climate change has increased the frequency of natural disasters worldwide. Among them, landslides are particularly hazardous because they drastically alter terrain, raising the risk of secondary disasters. This makes rapid
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Landslide Image Analysis and Disaster Risk Assessment Using Multimodal AI

Zero-shot Texture Anomaly Detection
In recent years, anomaly detection (AD) in images has become increasingly important in industrial inspection and quality control. In particular, detecting anomalies in texture images has encountered a challenge: conventional methods assume the availability of numerous normal images, but
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Driving Hazard Prediction by Multi-modal AI
In recent years, with the advancement of autonomous driving technologies and advanced driver-assistance systems (ADAS), predicting hazards in the vicinity of vehicles has become a critical issue for safe driving. Conventional methods have relied on video analysis and simulations
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Bridge Inspection by Multi-modal AI
This paper focuses on enhancing visual question answering (VQA) for bridge inspection using multimodal AI techniques that process both images and natural language. Traditionally, bridge inspections rely on expert visual assessments, which are
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RefVSR++: Reference-based High-Precision Video Super-Resolution
his study proposes a novel method called RefVSR++ for Reference-based VSR(*), which leverages the characteristics of multi-camera systems found in modern smartphones to restore low-resolution videos into high-resolution ones. Traditional VSR enhances
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