3.6k citations · 4.8k across the 50 of their papers we have counts for
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Unsupervised Primitive Discovery for Improved 3D Generative Modeling
Salman H. Khan, Yulan Guo, Munawar Hayat +1
3D shape generation is a challenging problem due to the high-dimensional output space and complex part configurations of real-world objects. As a result, existing algorithms experi…
An Adaptive Random Path Selection Approach for Incremental Learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan +3
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage. T…
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection
Bilal Taha, Munawar Hayat, Stefano Berretti +1
This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an invers…
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Aamir Mustafa, Salman Khan, Munawar Hayat +3
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images. The robustness of existing defenses suffers g…
Max-margin Class Imbalanced Learning with Gaussian Affinity
Munawar Hayat, Salman Khan, Waqas Zamir +2
Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving t…
Striking the Right Balance with Uncertainty
Salman Khan, Munawar Hayat, Waqas Zamir +2
Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes tend to get a concentrated representation in the classification space which hampers the gen…
Image Super-Resolution as a Defense Against Adversarial Attacks
Aamir Mustafa, Salman H. Khan, Munawar Hayat +2
Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible advers…