Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

Image segmentation is the task of associating pixels in an image with their respective object
class labels. It has a wide range of applications in many industries including healthcare …

In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …

Level 5 autonomy for self-driving cars requires a robust visual perception system that can
parse input images under any visual condition. However, existing semantic segmentation …

Adapting to a continuously evolving environment is a safety-critical challenge inevitably
faced by all autonomous-driving systems. Existing image-and video-based driving datasets …

The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …

Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …

In recent years, considerable progress has been made in semantic segmentation of images
with favorable environments. However, the environmental perception of autonomous driving …