260 citations · 629 across the 9 of their papers we have counts for
7 papers · 1 filter
Gabor Layers Enhance Network Robustness
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect on robustness against adversarial attacks of replacing…
DeepGCNs: Making GCNs Go as Deep as CNNs
Guohao Li, Matthias Müller, Guocheng Qian +4
Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, act…
MAIN: Multi-Attention Instance Network for Video Segmentation
Juan Leon Alcazar, Maria A. Bravo, Ali K. Thabet +4
Instance-level video segmentation requires a solid integration of spatial and temporal information. However, current methods rely mostly on domain-specific information (online lear…
BAOD: Budget-Aware Object Detection
Alejandro Pardo, Mengmeng Xu, Ali Thabet +2
We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Dete…
DeepGCNs: Can GCNs Go as Deep as CNNs?
Guohao Li, Matthias Müller, Ali Thabet +1
Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to…
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
Alejandro Pardo, Humam Alwassel, Fabian Caba Heilbron +2
Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent…
MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds
Ali Thabet, Humam Alwassel, Bernard Ghanem
We present a self-supervised task on point clouds, in order to learn meaningful point-wise features that encode local structure around each point. Our self-supervised network, name…