Titlebook: Artificial Intelligence and Soft Computing; 21st International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2023

Garfield

书目名称Artificial Intelligence and Soft Computing影响因子(影响力)




书目名称Artificial Intelligence and Soft Computing影响因子(影响力)学科排名




书目名称Artificial Intelligence and Soft Computing网络公开度




书目名称Artificial Intelligence and Soft Computing网络公开度学科排名




书目名称Artificial Intelligence and Soft Computing被引频次




书目名称Artificial Intelligence and Soft Computing被引频次学科排名




书目名称Artificial Intelligence and Soft Computing年度引用




书目名称Artificial Intelligence and Soft Computing年度引用学科排名




书目名称Artificial Intelligence and Soft Computing读者反馈




书目名称Artificial Intelligence and Soft Computing读者反馈学科排名

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978-3-031-23479-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Tae Sung Lee M.D.,Jihyuck Lee M.D.sformer architecture developed for CV is the Vision Transformer (ViT) [.]. ViT models have been used to solve numerous tasks in the CV area. One interesting task is the pose estimation of a human subject. We present our modified ViT model, . (UNsupervised TRAnsformer for Pose Estimation), that can r

innovation

Seungil Chung M.D., Ph.D.,Sanghoon Parkances in content-aware image resizing method such as seam carving allow an image to be resized while the critical content is retained. In this paper, we propose identifying the source camera from seam inserted images using blocks as small as 20 . 20. In particular, the correlation is computed betwee

Anthology

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Disfigurement due to Port Wine Stains,ecise detection and localization of follicles on the scalp and still poses a significant challenge for the computer vision and pattern recognition systems. We have proposed an automated vision system for follicles detection based on the classification of digitized microscopic scalp images using an e

palliative-care

Root494

Video-Based Facial Kinship Verification,e real ones to train Computer Vision methods. Autonomous driving research could largely benefit from this as its neural network-based perception systems need a large amount of labeled training data. However, the sim-to-real texture swapping is a demanding challenge because of the large gap between t