Search Results for author:
Found 16 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.
DeformableFormer: Classification of Endoscopic Ultrasound Guided Fine Needle Biopsy in Pancreatic Diseases
However, the images of pancreatic tissue fragments used in this study cannot be successfully classified by processing the entire image because the pancreatic tissue fragments are only a part of the image.
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.
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection
To improve the accuracy of STPM, this work uses a student network, as in generative models, to reconstruct normal features.
Ranked #32 on
Anomaly Detection
on MVTec AD
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
Adversarial Mutual Leakage Network for Cell Image Segmentation
Finally, we propose an Adversarial Mutual Leakage Network (AML-Net) that mutually leaks the information each other between the generator and the discriminator.
Localized Feature Aggregation Module for Semantic Segmentation
The proposed method recovers positional information by emphasizing the similarity between decoder's feature maps with superior semantic information and encoder's feature maps with superior positional information.
Reconstruction Student with Attention for Student-Teacher Pyramid Matching
The other student-teacher network has a role to reconstruct the features of normal products.
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.
Feature Sharing Cooperative Network for Semantic Segmentation
By sharing feature maps, one of two networks can obtain the information that cannot be obtained by a single network.
Cell image segmentation by Feature Random Enhancement Module
Although loss function is optimized in training deep neural network, far layers from the layers for computing loss function are difficult to train.
Feedback Attention for Cell Image Segmentation
In this paper, we address cell image segmentation task by Feedback Attention mechanism like feedback processing.
Feedback U-net for Cell Image Segmentation
By using Convolutional LSTM, the features in the second round are extracted based on the features acquired in the first round.
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
In this paper, we propose to predict the number of targets using multiple CNNs specialized to a specific appearance, and those CNNs are adaptively selected according to the appearance of a test image.
