Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

The current modus operandi in adapting pre-trained models involves updating all the
backbone parameters, ie., full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) …

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …

The hidden costs of artificial intelligence, from natural resources and labor to privacy and
freedom What happens when artificial intelligence saturates political life and depletes the …

In this provocative, consequential book, Couldry and Mejias theorize the dynamics of
change in contemporary capitalism as grounded in a new form of data colonialism. They …

Existing image classification datasets used in computer vision tend to have a uniform
distribution of images across object categories. In contrast, the natural world is heavily …

The United States spends more than $250 million each year on the American Community
Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race …

Deep domain adaptation methods have achieved appealing performance by learning
transferable representations from a well-labeled source domain to a different but related …

Abstract Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative
for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only …

As the size of transformer-based models continues to grow, fine-tuning these large-scale
pretrained vision models for new tasks has become increasingly parameter-intensive …