As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …

Training deep neural networks at the edge on light computational devices, embedded
systems and robotic platforms is nowadays very challenging. Continual learning techniques …

In the last few years, we have witnessed a renewed and fast-growing interest in continual
learning with deep neural networks with the shared objective of making current AI systems …

Superpixel is generated by automatically clustering pixels in an image into hundreds of
compact partitions, which is widely used to perceive the object contours for its excellent …

Robotic perception of filling mass estimation via multiple sensors and deep learning
approaches is still an open problem due to the diverse pouring durations, small pixel ratio …

Incremental few-shot learning is highly expected for practical robotics applications. On one
hand, robot is desired to learn new tasks quickly and flexibly using only few annotated …

无监督持续学习(UCL) 是指能够随着时间的推移而学习, 同时在没有监督的情况下记住以前的
模式. 虽然在这个方向上取得了很大进展, 但现有工作通常假设对于即将到来的数据有强大的先 …

Effective feature extraction is a key component in image recognition for robot vision. This
paper presents an improved contrastive learning-based image feature extraction and …

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic
and eventually surpass the intelligence observed in biological systems including …

Abstract Unsupervised Continual Learning (UCL) refers to the ability to learn over time while
remembering previous patterns without supervision. Although significant progress has been …