State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …

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

One-shot image segmentation aims to undertake the segmentation task of a novel class with
only one training image available. The difficulty lies in that image segmentation has …

Over the past few years, state-of-the-art image segmentation algorithms are based on deep
convolutional neural networks. To render a deep network with the ability to understand a …

Training semantic segmentation models requires a large amount of finely annotated data,
making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot …

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 …

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 …

Few-shot segmentation (FSS) methods perform image segmentation for a particular object
class in a target (query) image, using a small set of (support) image-mask pairs. Recent …

In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature
Enrichment Network for Few-Shot Segmentation”. The prior mask serves as an indicator that …

Deep learning based semantic segmentation solutions have yielded compelling results over
the preceding decade. They encompass diverse network architectures (FCN based or …