Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

In this paper, we analyse the generalization ability of binary classifiers for the task of
deepfake detection. We find that the stumbling block to their generalization is caused by the …

Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

Large-scale vision-language pre-training has shown impressive advances in a wide range
of downstream tasks. Existing methods mainly model the cross-modal alignment by the …

This paper aims to interpret how deepfake detection models learn artifact features of images
when just supervised by binary labels. To this end, three hypotheses from the perspective of …

Recently, a series of studies have tried to extract interactions between input variables
modeled by a DNN and define such interactions as concepts encoded by the DNN …

In this paper, we use the interaction inside adversarial perturbations to explain and boost the
adversarial transferability. We discover and prove the negative correlation between the …

This paper theoretically explains the intuition that simple concepts are more likely to be
learned by deep neural networks (DNNs) than complex concepts. In fact, recent studies …

Abstract Vision Transformers (ViTs) yield impressive performance across various vision
tasks. However, heavy computation and memory footprint make them inaccessible for edge …

This paper introduces the system of game-theoretic interactions, which connects both the
explanation of knowledge encoded in a deep neural networks (DNN) and the explanation of …