167 citations · 342 across the 22 of their papers we have counts for
24 papers
Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets
Chris Dulhanty, Alexander Wong
The ImageNet dataset ushered in a flood of academic and industry interest in deep learning for computer vision applications. Despite its significant impact, there has not been a co…
Food for thought: Ethical considerations of user trust in computer vision
Kaylen J. Pfisterer, Jennifer Boger, Alexander Wong
In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be…
Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks
Andrew Hryniowski, Alexander Wong
Researchers are actively trying to gain better insights into the representational properties of convolutional neural networks for guiding better network designs and for interpretin…
Implications of Computer Vision Driven Assistive Technologies Towards Individuals with Visual Impairment
Linda Wang, Alexander Wong
Computer vision based technology is becoming ubiquitous in society. One application area that has seen an increase in computer vision is assistive technologies, specifically for th…
Enabling Computer Vision Driven Assistive Devices for the Visually Impaired via Micro-architecture Design Exploration
Linda Wang, Alexander Wong
Recent improvements in object detection have shown potential to aid in tasks where previous solutions were not able to achieve. A particular area is assistive devices for individua…
Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
Rene Bidart, Alexander Wong
In this study, we propose the Affine Variational Autoencoder (AVAE), a variant of Variational Autoencoder (VAE) designed to improve robustness by overcoming the inability of VAEs t…