With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Endeavors have been recently made to transfer knowledge from the labeled pinhole image
domain to the unlabeled panoramic image domain via Unsupervised Domain Adaptation …

Accurate scene understanding from multiple sensors mounted on cars is a key requirement
for autonomous driving systems. Nowadays, this task is mainly performed through data …

Panoramic images, capturing a 360∘ field of view (FoV), encompass omnidirectional spatial
information crucial for scene understanding. However, it is not only costly to obtain training …

This paper addresses an interesting yet challenging problem--source-free unsupervised
domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation--given only a …

Unsupervised domain adaptation methods for panoramic semantic segmentation utilize real
pinhole images or low-cost synthetic panoramic images to transfer segmentation models to …

In this paper, we address the challenging source-free unsupervised domain adaptation
(SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre …

We present a novel holistic deep-learning approach for multi-task learning from a single
indoor panoramic image. Our framework named MultiPanoWise extends vision transformers …

Semantic segmentation plays a critical role in enabling intelligent vehicles to comprehend
their surrounding environments. However, deep learning-based methods usually perform …