A Restricted-Learning Network with Observation Credibility Inference for Few-Shot Degradation Modeling Academic Background In complex engineering systems, multiple sensors are widely used to monitor the degradation processes of equipment and predict their Remaining Useful Life (RUL). However, ensuring predictive performance remains challenging when...

A New Approach to Enhance Few-Shot Semantic Segmentation: Prior-Driven Edge Feature Enhancement Network In the field of artificial intelligence, semantic segmentation is a core technology in computer vision that aims to assign semantic category labels to every pixel in an image. However, traditional semantic segmentation methods rely on large amoun...

A Breakthrough in Mixed-Type Dialogue Generation: Few-Shot Learning Research One of the significant goals of Artificial Intelligence (AI) is to build agents capable of conducting multiple types of natural language dialogues. The industry and academia have long awaited the creation of dialogue models that can handle both open-domain dialogues and ta...

Background Introduction Stroke is one of the leading causes of death and disability worldwide, with the total number of stroke patients increasing globally due to population aging and urbanization. Although advances in treatment have reduced mortality rates, the number of survivors requiring rehabilitation has increased significantly. This is parti...

Solving Few-Shot Classification Problems with Balanced Feature Alignment and Uniformity Background and Motivation The goal of Few-Shot Learning (FSL) is to correctly recognize new samples with only a few examples from new classes. Existing few-shot learning methods mainly learn transferable knowledge from base classes by maximizing the information ...