Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

The deep CNNs in image semantic segmentation typically require a large number of
densely-annotated images for training and have difficulties in generalizing to unseen object …

Few-shot semantic segmentation (FSS) is a challenging task that aims to learn to segment
novel categories with only a few labeled images, and it has a wide range of real-world …

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 …

Strip steel surface defect () segmentation is a crucial method to inspect the surface quality of
strip steel in the producing-and-manufacturing. However, existing semantic segmentation …

Recently, some approaches are proposed to harness deep convolutional networks to
facilitate superpixel segmentation. The common practice is to first evenly divide the image …

Industrial defect segmentation is important to ensure product quality and production safety.
The main challenges in industrial applications are insufficient defect samples, large …

Few-shot semantic segmentation aims to learn a generalized model for unseen-class
segmentation with just a few densely annotated samples. Most current metric-based …

Semantic segmentation achieves significant success through large-scale training data.
Meanwhile, few-shot semantic segmentation was proposed to segment image regions of …

Abstract Few-Shot Classification (FSC) is an innovative application in machine learning.
FSC consists of two main components:(1) Pre-training, where a feature extraction model …