92 citations · 163 across the 12 of their papers we have counts for
12 papers
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4
William Berrios, Arturo Deza
Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend o…
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Anne Harrington, Arturo Deza
Recent work suggests that representations learned by adversarially robust networks are more human perceptually-aligned than non-robust networks via image manipulations. Despite app…
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation
Binxu Wang, David Mayo, Arturo Deza +2
Self-supervised learning is a powerful way to learn useful representations from natural data. It has also been suggested as one possible means of building visual representation in…
The Effects of Image Distribution and Task on Adversarial Robustness
Owen Kunhardt, Arturo Deza, Tomaso Poggio
In this paper, we propose an adaptation to the area under the curve (AUC) metric to measure the adversarial robustness of a model over a particular -interval (inter…
CUDA-Optimized real-time rendering of a Foveated Visual System
Elian Malkin, Arturo Deza, Tomaso Poggio
The spatially-varying field of the human visual system has recently received a resurgence of interest with the development of virtual reality (VR) and neural networks. The computat…
Hierarchically Compositional Tasks and Deep Convolutional Networks
Arturo Deza, Qianli Liao, Andrzej Banburski +1
The main success stories of deep learning, starting with ImageNet, depend on deep convolutional networks, which on certain tasks perform significantly better than traditional shall…
Emergent Properties of Foveated Perceptual Systems
Arturo Deza, Talia Konkle
The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which…
Assessment of Faster R-CNN in Man-Machine collaborative search
Arturo Deza, Amit Surana, Miguel P. Eckstein
With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when d…
Towards Metamerism via Foveated Style Transfer
Arturo Deza, Aditya Jonnalagadda, Miguel Eckstein
The problem of is defined as finding a family of perceptually indistinguishable, yet physically different images. In this paper, we propose our NeuroFo…
Attention Allocation Aid for Visual Search
Arturo Deza, Jeffrey R. Peters, Grant S. Taylor +2
This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance i…
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Arturo Deza, Miguel P. Eckstein
Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that…
Understanding Image Virality
Arturo Deza, Devi Parikh
Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper w…