51 citations · 245 across the 19 of their papers we have counts for
21 papers
DNN-Compressed Domain Visual Recognition with Feature Adaptation
Yingpeng Deng, Lina J. Karam
Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based v…
Learning-based Compression for Material and Texture Recognition
Yingpeng Deng, Lina J. Karam
Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based v…
Towards Imperceptible Universal Attacks on Texture Recognition
Yingpeng Deng, Lina J. Karam
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks o…
A Study for Universal Adversarial Attacks on Texture Recognition
Yingpeng Deng, Lina J. Karam
Given the outstanding progress that convolutional neural networks (CNNs) have made on natural image classification and object recognition problems, it is shown that deep learning m…
Frequency-Tuned Universal Adversarial Attacks
Yingpeng Deng, Lina J. Karam
Researchers have shown that the predictions of a convolutional neural network (CNN) for an image set can be severely distorted by one single image-agnostic perturbation, or univers…
It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality
Charan D. Prakash, Lina J. Karam
In this paper, we propose in our novel generative framework the use of Generative Adversarial Networks (GANs) to generate features that provide robustness for object detection on r…
Defending Against Universal Attacks Through Selective Feature Regeneration
Tejas Borkar, Felix Heide, Lina Karam
Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations. Specifically, image-agnostic (universal adversarial) perturba…
Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients
S. Alireza Golestaneh, Lina Karam
Perceptual quality assessment for synthesized textures is a challenging task. In this paper, we propose a training-free reduced-reference (RR) objective quality assessment method t…
Full Reference Objective Quality Assessment for Reconstructed Background Images
Aditee Shrotre, Lina Karam
With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on we…
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
Lina Karam, Tejas Borkar, Yu Cao +1
This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on…
Can the early human visual system compete with Deep Neural Networks?
Samuel Dodge, Lina Karam
We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images. Different from previous works, we limit the display ti…
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions
Milind S. Gide, Lina J. Karam
With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed over the past few years. These mod…
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
Samuel Dodge, Lina Karam
Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy…
DeepCorrect: Correcting DNN models against Image Distortions
Tejas Borkar, Lina Karam
In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object…
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
S. Alireza Golestaneh, Lina J. Karam
The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address th…
Quality Resilient Deep Neural Networks
Samuel Dodge, Lina Karam
We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networ…
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
Samuel Dodge, Lina Karam
Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing model…
Understanding How Image Quality Affects Deep Neural Networks
Samuel Dodge, Lina Karam
Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high…
The Effect of Distortions on the Prediction of Visual Attention
Milind S. Gide, Samuel F. Dodge, Lina J. Karam
Existing saliency models have been designed and evaluated for predicting the saliency in distortion-free images. However, in practice, the image quality is affected by a host of fa…
Traffic and Statistical Multiplexing Characterization of 3D Video Representation Formats (Extended Version)
Akshay Pulipaka, Patrick Seeling, Martin Reisslein +1
The network transport of 3D video, which contains two views of a video scene, poses significant challenges due to the increased video data compared to conventional single-view vide…
Is Bottom-Up Attention Useful for Scene Recognition?
Samuel F. Dodge, Lina J. Karam
The human visual system employs a selective attention mechanism to understand the visual world in an eficient manner. In this paper, we show how computational models of this mechan…