This paper addresses the challenging task of weakly-supervised video temporal grounding.
Existing approaches are generally based on the moment proposal selection framework that …

Adversarial attack on point clouds plays a vital role in evaluating and improving the
adversarial robustness of 3D deep learning models. Current attack methods are mainly …

Adversarial attacks have been successfully extended to the field of point clouds. Besides
applying the common perturbation guided by the gradient, adversarial attacks on point …

Performing 3-dimensional computer-aided design (3D-CAD) model classification, retrieval,
and reuse is of vital importance in industrial manufacturing, as it considerably shortens the …

Adversarial attacks on point clouds play a vital role in assessing and enhancing the
adversarial robustness of 3D deep learning models. While employing a variety of geometric …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Despite leveraging various geometric constraints …

Deep neural networks (DNNs) often display overconfidence when encountering out-of-
distribution (OOD) samples posing significant challenges in real-world applications …

Deep neural networks often exhibit the overconfidence issue when encountering out-of-
distribution (OOD) samples. To address this, leveraging large-scale pre-trained models like …

Adversarial attacks on 3D point clouds often exhibit unsatisfactory imperceptibility, which
primarily stems from the disregard for manifold-aware distortion, ie, distortion of the …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Existing methods typically apply perturbations to all …