Research

In recent years, self-supervised learning (SSL) has made significant progress as a method for extracting useful features from images without requiring human-annotated labels. These approaches have enabled models to achieve strong performance on

he Challenge of Unknown Objects in Detection Recent advances in object detection have enabled models to accurately detect and classify known objects in images. However, in real-world applications, detectors frequently encounter objects

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

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 when the orientations of the input and normal images do not match, accuracy degrades. Existing approaches […]

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

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 time-consuming, costly, and sometimes inconsistent. To address these challenges,

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

In the fields of Structure-from-Motion (SfM) and visual SLAM (Simultaneous Localization and Mapping), Bundle Adjustment (BA) is a crucial process that optimizes camera poses and the positions of 3D landmarks. In practice, many visual SLAM systems perform BA locally on the most recent keyframes and their associated landmarks to maintain overall system accuracy and tracking […]

Visual localization is a critical task in many computer vision applications such as Structure-from-Motion (SfM) and SLAM, as it involves estimating the 6-DoF camera pose. Traditional approaches extract global features for image retrieval and

This paper proposes a novel method called “Cross-Region Adaptation (CRA)” aimed at improving the accuracy of unsupervised domain adaptation (UDA) for semantic segmentation. Semantic segmentation, which assigns semantic labels to each pixel in an