activity
20062022
most citedUnderstanding the Role of Individual Units in a Deep Neural Network

385 citations · 529 across the 10 of their papers we have counts for

collaborators

17 papers

cs.CV2022

Predicting the impact of urban change in pedestrian and road safety

Cristina Bustos, Daniel Rhoads, Agata Lapedriza +2

Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, wit…

cs.CV20222 cited

Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents

Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza +3

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident…

cs.CV2021

Explainable, automated urban interventions to improve pedestrian and vehicle safety

Cristina Bustos, Daniel Rhoads, Albert Sole-Ribalta +4

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we can not d…

cs.CV2021

Predicting Driver Self-Reported Stress by Analyzing the Road Scene

Cristina Bustos, Neska Elhaouij, Albert Sole-Ribalta +3

Several studies have shown the relevance of biosignals in driver stress recognition. In this work, we examine something important that has been less frequently explored: We develop…

cs.CV2021

Recognizing Emotions evoked by Movies using Multitask Learning

Hassan Hayat, Carles Ventura, Agata Lapedriza

Understanding the emotional impact of movies has become important for affective movie analysis, ranking, and indexing. Methods for recognizing evoked emotions are usually trained o…

cs.CV20202 cited

Person Perception Biases Exposed: Revisiting the First Impressions Dataset

Julio C. S. Jacques Junior, Agata Lapedriza, Cristina Palmero +2

This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the ori…