Interpreting the decisions of deep learning models has been actively studied since the
explosion of deep neural networks. One of the most convincing interpretation approaches is …

Abstract Large pre-trained Vision-Language Models (VLMs) have become ubiquitous
foundational components of other models and downstream tasks. Although powerful, our …

We propose a margin-based loss for tuning joint vision-language models so that their
gradient-based explanations are consistent with region-level annotations provided by …

Despite being highly performant, deep neural networks might base their decisions on
features that spuriously correlate with the provided labels, thus hurting generalization. To …

As black-box models increasingly power high-stakes applications, a variety of data-driven
explanation methods have been introduced. Meanwhile, machine learning models are …

Via thousands of papers in Explainable AI (XAI), attention maps\cite {vaswani2017attention}
and feature attribution maps\cite {bansal2020sam} have been established as a common …

Understanding the severity of conditions shown in images in medical diagnosis is crucial
serving as a key guide for clinical assessment treatment as well as evaluating longitudinal …

Deep neural networks are highly performant, but might base their decision on spurious or
background features that co-occur with certain classes, which can hurt generalization. To …

Deep neural networks have emerged as highly effective tools for computer vision systems,
showcasing remarkable performance. However, the intrinsic opacity, potential biases, and …

Deep neural networks (DNNs) often encounter significant challenges related to opacity,
inherent biases, and shortcut learning, which undermine their practical reliability. In this …