40 citations · 41 across the 2 of their papers we have counts for
8 papers
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
Juan C. Pérez, Motasem Alfarra, Ali Thabet +2
Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face Recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose…
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
Guocheng Qian, Hasan Abed Al Kader Hammoud, Guohao Li +2
Access to 3D point cloud representations has been widely facilitated by LiDAR sensors embedded in various mobile devices. This has led to an emerging need for fast and accurate poi…
Learning to Cut by Watching Movies
Alejandro Pardo, Fabian Caba Heilbron, Juan León Alcázar +2
Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise. Many video editing…
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformatio…
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
Guocheng Qian, Abdulellah Abualshour, Guohao Li +2
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module,…
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds
Abdullah Hamdi, Sara Rojas, Ali Thabet +1
Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been exte…