Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

EXplainable AI (XAI) is an essential topic to improve human understanding of deep neural
networks (DNNs) given their black-box internals. For computer vision tasks, mainstream …

The recent wave of foundation models has witnessed tremendous success in computer
vision (CV) and beyond, with the segment anything model (SAM) having sparked a passion …

Robust segmentation in adverse weather conditions is crucial for autonomous driving.
However, these scenes struggle with recognition and make annotations expensive, resulting …

The Segment Anything Model (SAM) has shown impressive performance when applied to
natural image segmentation. However, it struggles with geographical images like aerial and …

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and
analysis in a variety of research disciplines, delivering unparalleled adaptability and …

Over the past decade, significant progress has been made in visual object tracking, largely
due to the availability of large-scale training datasets. However, existing tracking datasets …

Visual object tracking has significantly promoted autonomous applications for unmanned
aerial vehicles (UAVs). However, learning robust object representations for UAV tracking is …

Nighttime UAV tracking under low-illuminated scenarios has achieved great progress by
domain adaptation (DA). However, previous DA training-based works are deficient in …

Siamese-based trackers have been widely used in object tracking. However, aerial remote
tracking suffers from various challenges such as scale variation, viewpoint change …