715 citations · 1.9k across the 18 of their papers we have counts for
18 papers · 1 filter
Local Relighting of Real Scenes
Audrey Cui, Ali Jahanian, Agata Lapedriza +4
We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs…
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…
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…
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…
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…
Interpreting Face Inference Models using Hierarchical Network Dissection
Divyang Teotia, Agata Lapedriza, Sarah Ostadabbas
This paper presents Hierarchical Network Dissection, a general pipeline to interpret the internal representation of face-centric inference models. Using a probabilistic formulation…
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…
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…
Facial Expressions as a Vulnerability in Face Recognition
Alejandro Peña, Ignacio Serna, Aythami Morales +2
This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition syste…
Learning Emotional-Blinded Face Representations
Alejandro Peña, Julian Fierrez, Agata Lapedriza +1
We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for pers…
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In thi…
Detecting natural disasters, damage, and incidents in the wild
Ethan Weber, Nuria Marzo, Dim P. Papadopoulos +5
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emer…
Context Based Emotion Recognition using EMOTIC Dataset
Ronak Kosti, Jose M. Alvarez, Adria Recasens +1
In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacit…
Places: An Image Database for Deep Scene Understanding
Bolei Zhou, Aditya Khosla, Agata Lapedriza +2
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene…
Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza +2
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localiza…
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing
Amir H. Bakhtiary, Agata Lapedriza, David Masip
In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural ne…
Object Detectors Emerge in Deep Scene CNNs
Bolei Zhou, Aditya Khosla, Agata Lapedriza +2
With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled exampl…
Are all training examples equally valuable?
Agata Lapedriza, Hamed Pirsiavash, Zoya Bylinskii +1
When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others. The goal of this paper is to…