Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

Deep learning-based intelligent vehicle perception has been develo** prominently in
recent years to provide a reliable source for motion planning and decision making in …

This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client
continues to learn incremental tasks where their domain shifts from each other. We propose …

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized
semantic predictions across diverse urban-scene styles. Unlike generic domain gap …

Multi-modal fusion is a promising approach to boost the autonomous driving performance
and has already received a large amount of attention. Meanwhile, to increase driving …

Rain can significantly impair the driver's sight and affect his performance when driving in wet
conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered …

Abstract Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data
by adapting a trained model to distribution shifts occurring at test-time. We propose to …

Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

Deep learning-based fire detection models are usually trained offline on static datasets. For
continuously increasing heterogeneous sensor data, incremental learning is a resolution to …

Federated Learning (FL) is to allow multiple clients to collaboratively train a model while
kee** their data locally. However, existing FL approaches typically assume that the data in …