Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …

This paper introduces an efficient strategy to transform Large Language Models (LLMs) into
Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a …

Image attribution algorithms aim to identify important regions that are highly relevant to
model decisions. Although existing attribution solutions can effectively assign importance to …

The proliferation of face forgery techniques has raised significant concerns within society,
thereby motivating the development of face forgery detection methods. These methods aim …

Sketch classification models have been extensively investigated by designing a task-driven
deep neural network. Despite their successful performances, few works have attempted to …

Explaining deep models in a human-understandable way has been explored by many works
that mostly explain why an input causes a corresponding prediction (ie., Why P?). However …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Social media include diverse interaction metrics related to user popularity, the most evident
example being the number of user followers. The latter has raised concerns about the …

Advances in multimodal pre-training have propelled object-level foundation models, such as
Grounding DINO and Florence-2, in tasks like visual grounding and object detection …

Feature attribution explains Artificial Intelligence (AI) at the instance level by providing
importance scores of input features' contributions to model prediction. Integrated Gradients …