26 citations · 75 across the 16 of their papers we have counts for
24 papers · 1 filter
Statistical Challenges with Dataset Construction: Why You Will Never Have Enough Images
Josh Goldman, John K. Tsotsos
Deep neural networks have achieved impressive performance on many computer vision benchmarks in recent years. However, can we be confident that impressive performance on benchmarks…
An Empirical Method to Quantify the Peripheral Performance Degradation in Deep Networks
Calden Wloka, John K. Tsotsos
When applying a convolutional kernel to an image, if the output is to remain the same size as the input then some form of padding is required around the image boundary, meaning tha…
Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks
Mahdi Biparva, John Tsotsos
Feature disentanglement of the foreground target objects and the background surrounding context has not been yet fully accomplished. The lack of network interpretability prevents a…
PESAO: Psychophysical Experimental Setup for Active Observers
Markus D. Solbach, John K. Tsotsos
Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how…
Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles
David Fernández-Llorca, Mahdi Biparva, Rubén Izquierdo-Gonzalo +1
In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must an…
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
One of the major challenges for autonomous vehicles in urban environments is to understand and predict other road users' actions, in particular, pedestrians at the point of crossin…