Understanding dark scenes based on multi-modal image data is challenging, as both the
visible and auxiliary modalities provide limited semantic information for the task. Previous …

Adaptation of semantic segmentation networks to different visual conditions is vital for robust
perception in autonomous cars and robots. However, previous work has shown that most …

Unsupervised cross-domain scene segmentation approach adapts the source model to the
target domain, which utilizes two-stage strategies to minimize the inter-domain and intra …

The increasing relevance of panoptic segmentation is tied to the advancements in
autonomous driving and AR/VR applications. However, the deployment of such models has …

Due to unaffordable computational costs, the regularized disparity in iterative stereo
matching is typically maintained at a lower resolution than the input. To regress the full …

Semantic segmentation based on optical images can provide comprehensive scene
information for intelligent vehicle systems, thus aiding in scene perception and decision …

Night-time scene parsing aims to extract pixel-level semantic information in night images,
aiding downstream tasks in understanding scene object distribution. Due to limited labeled …

Unsupervised domain adaptation (UDA) is an important solution for the cross-domain
problem in semantic segmentation. Existing segmentation UDA methods mainly consider …

In unsupervised domain adaptation (UDA), where models are trained on source data (eg,
synthetic) and adapted to target data (eg, real-world) without target annotations, addressing …

The increasing relevance of panoptic segmentation is tied to the advancements in
autonomous driving and AR/VR applications. However, the deployment of such models has …