activity
20172022
most citedPedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs

26 citations · 70 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV20211 cited

Industry and Academic Research in Computer Vision

Iuliia Kotseruba, Manos Papagelis, John K. Tsotsos

This work aims to study the dynamic between research in the industry and academia in computer vision. The results are demonstrated on a set of top-5 vision conferences that are rep…

cs.AI2021

On the Control of Attentional Processes in Vision

John K. Tsotsos, Omar Abid, Iuliia Kotseruba +1

The study of attentional processing in vision has a long and deep history. Recently, several papers have presented insightful perspectives into how the coordination of multiple att…

cs.CV202026 cited

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…

cs.CV2019

A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision

John K. Tsotsos, Iuliia Kotseruba, Alexander Andreopoulos +1

Why do some continue to wonder about the success and dominance of deep learning methods in computer vision and AI? Is it not enough that these methods provide practical solutions t…

cs.CV2019

Rapid Visual Categorization is not Guided by Early Salience-Based Selection

John K. Tsotsos, Iuliia Kotseruba, Calden Wloka

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, v…

cs.CV20187 cited

SMILER: Saliency Model Implementation Library for Experimental Research

Calden Wloka, Toni Kunić, Iuliia Kotseruba +4

The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintainin…