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
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High-Speed, High-Precision Visual Localization

Unsupervised Domain Adaptation for Semantic Segmentation
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
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Image Anomaly Detection via Local and Global Knowledge Integration
This study proposes a novel method for high-precision detection of “logical anomalies” (e.g., misplacements or omissions of parts that depend on the overall contextual information of an image) in applications such as industrial inspection. Conventional
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GRIT: Transformer-based Image Captioning Leveraging Grid and Region Features
“Image captioning,” the task of describing the scenery and objects in an image using natural language, is one of the technologies in artificial intelligence that enables visual information to be expressed in words. In recent mainstream approaches, features—informative representations extracted from the image—are first obtained and then used to generate natural-sounding captions. The quality and […]
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SBCFormer: An Image Recognition Model for Single Board Computers
In recent years, deep learning-based image recognition has expanded into practical applications such as agriculture, fisheries, and livestock management. In these domains, low-cost and low-power systems are often more important than high-speed
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