To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

Global modeling-based image restoration frameworks have become popular. However, they
often require a high memory footprint and do not consider task-specific degradation. Our …

Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-
scale modeling. However, spatial up-sampling operators (eg, interpolation, transposed …

Since rain streaks exhibit diverse geometric appearances and irregular overlapped
phenomena, these complex characteristics challenge the design of an effective single image …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Image deraining is a challenging task since rain streaks have the characteristics of a
spatially long structure and have a complex diversity. Existing deep learning-based methods …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Existing methods mainly handle single weather types. However, the connections of different
weather conditions at deep representation level are usually ignored. These connections, if …