Abstract Bird's-eye-view (BEV) grid is a common representation for the perception of road
components, eg, drivable area, in autonomous driving. Most existing approaches rely on …

We propose MAMo, a novel memory and attention framework for monocular video depth
estimation. MAMo can augment and improve any single-image depth estimation networks …

In this paper, we develop rotation-equivariant neural networks for 4D panoptic
segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that …

Multi-task learning has become increasingly popular in the machine learning field but its
practicality is hindered by the need for large labeled datasets. Most multi-task learning …

In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively
unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP …

Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent
advancements achieving real-time on-device optical flow estimation remains a complex …

In this study, we address the intricate challenge of multi-task dense prediction,
encompassing tasks such as semantic segmentation, depth estimation, and surface normal …

Bird's eye view (BEV) segmentation map is a recent development in autonomous driving that
provides effective environmental information, such as drivable areas and lane dividers. Most …

Dynamic brain maps are crucial for comprehending brain dynamism, involving the study of
rapid changes in brain activity across different regions over time. However, computational …

Transformer neural network architecture has revolutionized the deep learning field,
becoming the central architecture used in most relevant tasks. However, as the size of state …