663 citations · 3.7k across the 62 of their papers we have counts for
10 papers · 1 filter
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Zhijie Zhang, Huazhu Fu, Hang Dai +3
Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obt…
Distilled Siamese Networks for Visual Tracking
Jianbing Shen, Yuanpei Liu, Xingping Dong +3
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from…
Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces
Huazhu Fu, Boyang Wang, Jianbing Shen +4
Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated a…
Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network
Shadab Khan, Ahmed H. Shahin, Javier Villafruela +2
To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model tha…
Understanding More about Human and Machine Attention in Deep Neural Networks
Qiuxia Lai, Salman Khan, Yongwei Nie +3
Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models…
Salient Object Detection in the Deep Learning Era: An In-Depth Survey
Wenguan Wang, Qiuxia Lai, Huazhu Fu +3
As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predo…
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…