8 citations · 19 across the 7 of their papers we have counts for
7 papers
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
Sota Kato, Hinako Mitsuoka, Kazuhiro Hotta
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…
Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection
Sota Kato, Kazuhiro Hotta, Yuhki Hatakeyama +1
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…
Enlarged Large Margin Loss for Imbalanced Classification
Sota Kato, Kazuhiro Hotta
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 classi…
One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt
Sota Kato, Kazuhiro Hotta
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.…
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
Sota Kato, Kazuhiro Hotta
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 stil…
Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation
Sota Kato, Kazuhiro Hotta
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 mic…
MSE Loss with Outlying Label for Imbalanced Classification
Sota Kato, Kazuhiro Hotta
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…