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of 139
pro vyhledávání: '"Kato, Sota"'
There has been a lot of recent research on improving the efficiency of fine-tuning foundation models. In this paper, we propose a novel efficient fine-tuning method that allows the input image size of Segment Anything Model (SAM) to be variable. SAM
Externí odkaz:
http://arxiv.org/abs/2408.12406
Facial landmark detection is an essential technology for driver status tracking and has been in demand for real-time estimations. As a landmark coordinate prediction, heatmap-based methods are known to achieve a high accuracy, and Lite-HRNet can achi
Externí odkaz:
http://arxiv.org/abs/2308.12133
The severity of war, measured by battle deaths, follows a power-law distribution. Here, we demonstrate that power law also holds in the temporal aspects of interstate conflicts. A critical quantity is the inter-conflict interval (ICI), the interval b
Externí odkaz:
http://arxiv.org/abs/2308.11135
Autor:
Kato, Sota, Hotta, Kazuhiro
We propose a novel loss function for imbalanced classification. LDAM loss, which minimizes a margin-based generalization bound, is widely utilized for class-imbalanced image classification. Although, by using LDAM loss, it is possible to obtain large
Externí odkaz:
http://arxiv.org/abs/2306.09132
Autor:
Kato, Sota, Hotta, Kazuhiro
Semantic segmentation of microscopic cell images using deep learning is an important technique, however, it requires a large number of images and ground truth labels for training. To address the above problem, we consider an efficient learning framew
Externí odkaz:
http://arxiv.org/abs/2304.07991
Autor:
Kato, Sota
乙第13488号
論農博第2900号
新制||農||1093(附属図書館)
学位論文||R4||N5367(農学部図書室)
学位規則第4条第2項該当
Doctor of Agricultural Science
Kyoto University
DFAM
論農博第2900号
新制||農||1093(附属図書館)
学位論文||R4||N5367(農学部図書室)
学位規則第4条第2項該当
Doctor of Agricultural Science
Kyoto University
DFAM
Externí odkaz:
http://hdl.handle.net/2433/274942
Autor:
Kato, Sota, Hotta, Kazuhiro
Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and di
Externí odkaz:
http://arxiv.org/abs/2207.07842
Autor:
Kato, Sota, Hotta, Kazuhiro
We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality. It is difficult to capture cells with strong light. Therefore, the microscopic images of cells tend to have low image quality but these imag
Externí odkaz:
http://arxiv.org/abs/2108.13118
Autor:
Kato, Sota, Hotta, Kazuhiro
In this paper, we propose mean squared error (MSE) loss with outlying label for class imbalanced classification. Cross entropy (CE) loss, which is widely used for image recognition, is learned so that the probability value of true class is closer to
Externí odkaz:
http://arxiv.org/abs/2107.02393
Autor:
Kato, Sota, Hotta, Kazuhiro
Publikováno v:
In Computers in Biology and Medicine January 2024 168