In this work, we propose a real-time monocular 3D video reconstruction approach named
Flora for reconstructing delicate and complete 3D scenes from RGB video sequences in an …

Impressive results in natural language processing (NLP) based on the Transformer neural
network architecture have inspired researchers to explore viewing offline reinforcement …

Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

Multi-modal learning has made impressive progress in autonomous driving by leveraging
information from multiple sensors. Existing feature fusion methods make decisions by …

Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy
both cumulative and state-wise costs from offline datasets. This arrangement provides an …

We study how vision-language models (VLMs) trained on web-scale data can be integrated
into end-to-end driving systems to boost generalization and enable interactivity with human …

This paper proposes a new glass segmentation method utilizing paired RGB and thermal
images. Due to the large difference between the transmission property of visible light and …

LiDAR and camera are two common sensors to collect data in time for 3D object detection
under the autonomous driving context. Though the complementary information across …

The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …