Deep neural networks are complex and opaque. As they enter application in a variety of
important and safety critical domains, users seek methods to explain their output predictions …

Parameter-efficient tuning has become a trend in transferring large-scale foundation models
to downstream applications. Existing methods typically embed some light-weight tuners into …

Despite recent promising performances across a range of vision tasks, vision Transformers
still have an issue of high computational costs. Recently, vision prompt learning has …

Image classification models built into visual support systems and other assistive devices
need to provide accurate predictions about their environment. We focus on an application of …

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the
Earth undergoes global warming. It is difficult to predict when and where an incident will …

This article aims to implement a prototype screening system to identify flooding-related
photos from social media. These photos, associated with their geographic locations, can …

We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing
research on audiovisual fine-grained categorization. While our community has made great …

Hashing is a practical approach for the approximate nearest neighbor search. Deep hashing
methods, which train deep networks to generate compact and similarity-preserving binary …

As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning
(VPT) as a parameter-efficient transfer learning technique has gained attention due to its …

Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success
on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the …