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Found 6 papers, 3 papers with code
Lite-HRNet Plus: Fast and Accurate Facial Landmark Detection
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.
Enlarged Large Margin Loss for Imbalanced Classification
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.
One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt
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
Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss.
Ranked #1 on
Medical Image Segmentation
on CVC-ClinicDB
Automatic Preprocessing and Ensemble Learning for Low Quality Cell Image Segmentation
We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality.
MSE Loss with Outlying Label for Imbalanced Classification
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.
