Search Results for author: Sota Kato

Found 6 papers, 3 papers with code

Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection

no code implementations23 Aug 2023 Sota Kato, Kazuhiro Hotta, Yuhki Hatakeyama, Yoshinori Konishi

Lite-HRNet Plus achieves two improvements: a novel fusion block based on a channel attention and a novel output module with less computational intensity using multi-resolution feature maps.

Facial Landmark Detection

Enlarged Large Margin Loss for Imbalanced Classification

1 code implementation15 Jun 2023 Sota Kato, Kazuhiro Hotta

Although, by using LDAM loss, it is possible to obtain large margins for the minority classes and small margins for the majority classes, the relevance to a large margin, which is included in the original softmax cross entropy loss, is not be clarified yet.

Classification Image Classification +1

One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt

1 code implementation17 Apr 2023 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.

Image Segmentation One-Shot Segmentation +1

Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation

1 code implementation16 Jul 2022 Sota Kato, Kazuhiro Hotta

Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss.

Image Segmentation Medical Image Segmentation +1

Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation

no code implementations30 Aug 2021 Sota Kato, Kazuhiro Hotta

We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality.

Ensemble Learning Image Segmentation +2

MSE Loss with Outlying Label for Imbalanced Classification

no code implementations6 Jul 2021 Sota Kato, Kazuhiro Hotta

Unlike CE loss, MSE loss is possible to equalize the number of back propagation for all classes and to learn the feature space considering the relationships between classes as metric learning.

Classification imbalanced classification +2

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