418 citations · 3.5k across the 221 of their papers we have counts for
29 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…
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,…
Assessing the Robustness of Visual Question Answering Models
Jia-Hong Huang, Modar Alfadly, Bernard Ghanem +1
Deep neural networks have been playing an essential role in the task of Visual Question Answering (VQA). Until recently, their accuracy has been the main focus of research. Now the…
A Context-Aware Loss Function for Action Spotting in Soccer Videos
Anthony Cioppa, Adrien Deliège, Silvio Giancola +4
In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function tha…
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…
SGAS: Sequential Greedy Architecture Search
Guohao Li, Guocheng Qian, Itzel C. Delgadillo +3
Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, disco…
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
Jesus Zarzar, Silvio Giancola, Bernard Ghanem
In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount impo…
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Humam Alwassel, Dhruv Mahajan, Bruno Korbar +3
Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other…
G-TAD: Sub-Graph Localization for Temporal Action Detection
Mengmeng Xu, Chen Zhao, David S. Rojas +2
Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focu…
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…
Finding Moments in Video Collections Using Natural Language
Victor Escorcia, Mattia Soldan, Josef Sivic +2
We introduce the task of retrieving relevant video moments from a large corpus of untrimmed, unsegmented videos given a natural language query. Our task poses unique challenges as…
Constrained Clustering: General Pairwise and Cardinality Constraints
Adel Bibi, Ali Alqahtani, Bernard Ghanem
We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and car…
3D Instance Segmentation via Multi-Task Metric Learning
Jean Lahoud, Bernard Ghanem, Marc Pollefeys +1
We propose a novel method for instance label segmentation of dense 3D voxel grids. We target volumetric scene representations, which have been acquired with depth sensors or multi-…
Expected Tight Bounds for Robust Training
Salman Alsubaihi, Adel Bibi, Modar Alfadly +2
Training Deep Neural Networks that are robust to norm bounded adversarial attacks remains an elusive problem. While exact and inexact verification-based methods are generally too e…
MAP Inference via L2-Sphere Linear Program Reformulation
Baoyuan Wu, Li Shen, Tong Zhang +1
Maximum a posteriori (MAP) inference is an important task for graphical models. Due to complex dependencies among variables in realistic model, finding an exact solution for MAP in…
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
Guocheng Qian, Yuanhao Wang, Jinjin Gu +4
Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that…
Analytical Moment Regularizer for Gaussian Robust Networks
Modar Alfadly, Adel Bibi, Bernard Ghanem
Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the s…
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
Matthias Müller, Guohao Li, Vincent Casser +3
Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end…
IAN: Combining Generative Adversarial Networks for Imaginative Face Generation
Abdullah Hamdi, Bernard Ghanem
Generative Adversarial Networks (GANs) have gained momentum for their ability to model image distributions. They learn to emulate the training set and that enables sampling from th…
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…
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Chen Zhao, Bernard Ghanem
Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of comp…
Towards Analyzing Semantic Robustness of Deep Neural Networks
Abdullah Hamdi, Bernard Ghanem
Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pos…
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…