b'Munawar Hayat'

Transformers in Vision: A Survey

Astounding results from transformer models on natural language tasks hav...
01/04/2021 ∙ by Salman Khan, et al. ∙ 0 ∙ share

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Synthesizing the Unseen for Zero-shot Object Detection

The existing zero-shot detection approaches project visual features to t...
10/19/2020 ∙ by Nasir Hayat, et al. ∙ 0 ∙ share

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Meta-learning the Learning Trends Shared Across Tasks

Meta-learning stands for 'learning to learn' such that generalization to...
10/19/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

This paper introduces the real image Super-Resolution (SR) challenge tha...
09/25/2020 ∙ by Pengxu Wei, et al. ∙ 7 ∙ share

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Stylized Adversarial Defense

Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, ...
07/29/2020 ∙ by Muzammal Naseer, et al. ∙ 8 ∙ share

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Self-supervised Knowledge Distillation for Few-shot Learning

Real-world contains an overwhelmingly large number of object classes, le...
06/17/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 77 ∙ share

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A Self-supervised Approach for Adversarial Robustness

Adversarial examples can cause catastrophic mistakes in Deep Neural Netw...
06/08/2020 ∙ by Muzammal Naseer, et al. ∙ 15 ∙ share

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iTAML: An Incremental Task-Agnostic Meta-learning Approach

Humans can continuously learn new knowledge as their experience grows. I...
03/25/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 4 ∙ share

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CycleISP: Real Image Restoration via Improved Data Synthesis

The availability of large-scale datasets has helped unleash the true pot...
03/17/2020 ∙ by Syed Waqas Zamir, et al. ∙ 14 ∙ share

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Learning Enriched Features for Real Image Restoration and Enhancement

With the goal of recovering high-quality image content from its degraded...
03/15/2020 ∙ by Syed Waqas Zamir, et al. ∙ 11 ∙ share

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Towards Robust and Reproducible Active Learning Using Neural Networks

Active learning (AL) is a promising ML paradigm that has the potential t...
02/21/2020 ∙ by Prateek Munjal, et al. ∙ 0 ∙ share

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Unsupervised Primitive Discovery for Improved 3D Generative Modeling

3D shape generation is a challenging problem due to the high-dimensional...
06/09/2019 ∙ by Salman H. Khan, et al. ∙ 0 ∙ share

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Random Path Selection for Incremental Learning

Incremental life-long learning is a main challenge towards the long-stan...
06/03/2019 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection

This paper proposes an approach to learn generic multi-modal mesh surfac...
04/08/2019 ∙ by Bilal Taha, et al. ∙ 8 ∙ share

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Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

Deep neural networks are vulnerable to adversarial attacks, which can fo...
04/01/2019 ∙ by Aamir Mustafa, et al. ∙ 0 ∙ share

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Max-margin Class Imbalanced Learning with Gaussian Affinity

Real-world object classes appear in imbalanced ratios. This poses a sign...
01/23/2019 ∙ by Munawar Hayat, et al. ∙ 0 ∙ share

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Striking the Right Balance with Uncertainty

Learning unbiased models on imbalanced datasets is a significant challen...
01/22/2019 ∙ by Salman Khan, et al. ∙ 0 ∙ share

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Image Super-Resolution as a Defense Against Adversarial Attacks

Convolutional Neural Networks have achieved significant success across m...
01/07/2019 ∙ by Aamir Mustafa, et al. ∙ 10 ∙ share

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Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation

Variational auto-encoders (VAEs) provide an attractive solution to image...
04/27/2018 ∙ by Salman H. Khan, et al. ∙ 0 ∙ share

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Regularization of Deep Neural Networks with Spectral Dropout

The big breakthrough on the ImageNet challenge in 2012 was partially due...
11/23/2017 ∙ by Salman Khan, et al. ∙ 0 ∙ share

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A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification

Unlike standard object classification, where the image to be classified ...
06/18/2015 ∙ by Munawar Hayat, et al. ∙ 0 ∙ share

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A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due ...
06/17/2015 ∙ by Salman H. Khan, et al. ∙ 0 ∙ share

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Munawar Hayat

Featured Co-authors

Liang Lin

Xin Li

Ling Shao

Ming-Hsuan Yang

Hao Li

Radu Timofte

Wangmeng Zuo

Mubarak Shah

Fahad Shahbaz Khan

Fatih Porikli

Salman Khan

Transformers in Vision: A Survey

Synthesizing the Unseen for Zero-shot Object Detection

Meta-learning the Learning Trends Shared Across Tasks

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Stylized Adversarial Defense

Self-supervised Knowledge Distillation for Few-shot Learning

A Self-supervised Approach for Adversarial Robustness

iTAML: An Incremental Task-Agnostic Meta-learning Approach

CycleISP: Real Image Restoration via Improved Data Synthesis

Learning Enriched Features for Real Image Restoration and Enhancement

Towards Robust and Reproducible Active Learning Using Neural Networks

Unsupervised Primitive Discovery for Improved 3D Generative Modeling

Random Path Selection for Incremental Learning

Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection

Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

Max-margin Class Imbalanced Learning with Gaussian Affinity

Striking the Right Balance with Uncertainty

Image Super-Resolution as a Defense Against Adversarial Attacks

Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation

Regularization of Deep Neural Networks with Spectral Dropout

A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification

A Discriminative Representation of Convolutional Features for Indoor Scene Recognition