A well-performed deep learning model in image segmentation relies on a large number of
labeled data. However, it is hard to obtain sufficient high-quality raw data in industrial …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

Fully-supervised deep learning segmentation models are inflexible when encountering new
unseen semantic classes and their fine-tuning often requires significant amounts of …

Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to
perform visual recognition tasks, recent evidence suggests that texture bias in CNNs …

Few-shot segmentation aims to learn a segmentation model that can be generalized to
novel classes with only a few training images. In this paper, we propose a Cross-Reference …

Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using
deep learning have shown improved performance over conventional techniques. However …

Traditional semantic segmentation requires a large labeled image dataset and can only be
predicted within predefined classes. Solving this problem of few-shot segmentation, which …

We tackle the challenging task of few-shot segmentation in this work. It is essential for few-
shot semantic segmentation to fully utilize the support information. Previous methods …

Semantic segmentation is a difficult task in computer vision that have applications in many
scenarios, often as a preprocessing step for a tool. Current solutions are based on Deep …