Currently, image-text-driven multi-modal deep learning models have demonstrated their
outstanding potential in many fields. In practice, tasks centered around facial images have …

In recent years, a variety of graph learningbased multi-view clustering (MVC) methods have
emerged. However, these methods continue to face challenges in extracting latent features …

The ability of gaze estimation models to generalize is often significantly hindered by various
factors unrelated to gaze, especially when the training dataset is limited. Current strategies …

Gaze estimation methods often experience significant performance degradation when
evaluated across different domains, due to the domain gap between the testing and training …

Although quantum machine learning has been introduced for a while, its applications in
computer vision are still limited. This paper, therefore, revisits the quantum visual encoding …

Performance disparities across sub-populations are known to exist in deep learning-based
vision recognition models, but previous work has largely addressed such fairness concerns …

Inverting visual representations within deep neural networks (DNNs) presents a challenging
and important problem in the field of security and privacy for deep learning. The main goal is …

Unsupervised vision clustering, a cornerstone in computer vision, has been studied for
decades, yielding significant outcomes across numerous vision tasks. However, these …

Face recognition is a core task in computer vision designed to identify and authenticate
individuals by analyzing facial patterns and features. This field intersects with artificial …