Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an
encoder which can be used as a general-purpose feature extractor, such that downstream …

Multimodal contrastive learning aims to train a general-purpose feature extractor, such as
CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit …

Adversarial patch is an important form of real-world adversarial attack that brings serious
risks to the robustness of deep neural networks. Previous methods generate adversarial …

Face recognition is a prevailing authentication solution in numerous biometric applications.
Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face …

As billions of personal data being shared through social media and network, the data
privacy and security have drawn an increasing attention. Several attempts have been made …

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-
craft feature extraction with its strong feature learning capability, leading to substantial …

We show a hardness result for random smoothing to achieve certified adversarial
robustness against attacks in the lp ball of radius ε when p> 2. Although random smoothing …