Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …

Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …

Multi-modality image fusion and segmentation play a vital role in autonomous driving and
robotic operation. Early efforts focus on boosting the performance for only one task, eg …

Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …

Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …

Surround View fisheye cameras are commonly deployed in automated driving for 360 near-
field sensing around the vehicle. This work presents a multi-task visual perception network …

State-of-the-art self-supervised learning approaches for monocular depth estimation usually
suffer from scale ambiguity. They do not generalize well when applied on distance …

Automated construction of surface geometries of cardiac structures from volumetric medical
images is important for a number of clinical applications. While deep-learning-based …

Autonomous driving is rapidly advancing, and Level 2 functions are becoming a standard
feature. One of the foremost outstanding hurdles is to obtain robust visual perception in …

In this work, we seek to predict camera poses across scenes with a multi-task learning
manner, where we view the localization of each scene as a new task. We propose OFVL …