Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Abstract Current Knowledge Distillation (KD) methods for semantic segmentation often
guide the student to mimic the teacher's structured information generated from individual …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Abstract Knowledge distillation (KD) has been proven a simple and effective tool for training
compact dense prediction models. Lightweight student networks are trained by extra …

In this paper, we investigate the issue of knowledge distillation for training compact semantic
segmentation networks by making use of cumbersome networks. We start from the …

In recent years, various neural network architectures for computer vision have been devised,
such as the visual transformer and multilayer perceptron (MLP). A transformer based on an …

Semi-supervised semantic segmentation aims to learn a semantic segmentation model via
limited labeled images and adequate unlabeled images. The key to this task is generating …

Recent advances have been made in applying convolutional neural networks to achieve
more precise prediction results for medical image segmentation problems. However, the …

Existing knowledge distillation works for semantic segmentation mainly focus on transfering
high-level contextual knowledge from teacher to student. However, low-level texture …