95 citations · 206 across the 6 of their papers we have counts for
8 papers
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Hongyang Li, David Eigen, Samuel Dodge +2
Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limit…
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 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…
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…
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…