Search Results for author: Agata Lapedriza

Found 24 papers, 12 papers with code

Local Relighting of Real Scenes

2 code implementations6 Jul 2022 Audrey Cui, Ali Jahanian, Agata Lapedriza, Antonio Torralba, Shahin Mahdizadehaghdam, Rohit Kumar, David Bau

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.

Image Relighting

Predicting the impact of urban change in pedestrian and road safety

no code implementations3 Feb 2022 Cristina Bustos, Daniel Rhoads, Agata Lapedriza, Javier Borge-Holthoefer, Albert Solé-Ribalta

In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence.

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

1 code implementation11 Jan 2022 Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza, Ferda Ofli, Muhammad Imran, Antonio Torralba

In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.


Explainable, automated urban interventions to improve pedestrian and vehicle safety

no code implementations22 Oct 2021 Cristina Bustos, Daniel Rhoads, Albert Sole-Ribalta, David Masip, Alex Arenas, Agata Lapedriza, Javier Borge-Holthoefer

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e. g. the problems of congestion and pollution.

Image Segmentation Semantic Segmentation

Interpreting Face Inference Models using Hierarchical Network Dissection

1 code implementation23 Aug 2021 Divyang Teotia, Agata Lapedriza, Sarah Ostadabbas

Our pipeline is inspired by Network Dissection, a popular interpretability model for object-centric and scene-centric models.

Recognizing Emotions evoked by Movies using Multitask Learning

1 code implementation30 Jul 2021 Hassan Hayat, Carles Ventura, Agata Lapedriza

In this paper, we model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach.

Multi-Task Learning

Person Perception Biases Exposed: Revisiting the First Impressions Dataset

no code implementations30 Nov 2020 Julio C. S. Jacques Junior, Agata Lapedriza, Cristina Palmero, Xavier Baró, Sergio Escalera

This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing.

Facial Expressions as a Vulnerability in Face Recognition

no code implementations17 Nov 2020 Alejandro Peña, Ignacio Serna, Aythami Morales, Julian Fierrez, Agata Lapedriza

This work explores facial expression bias as a security vulnerability of face recognition systems.

Face Recognition

Human-centric Dialog Training via Offline Reinforcement Learning

1 code implementation EMNLP 2020 Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, Rosalind Picard

We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).

Language Modelling Offline RL +2

Learning Emotional-Blinded Face Representations

no code implementations18 Sep 2020 Alejandro Peña, Julian Fierrez, Agata Lapedriza, Aythami Morales

We propose two face representations that are blind to facial expressions associated to emotional responses.

Emotion Recognition Fairness

Understanding the Role of Individual Units in a Deep Neural Network

2 code implementations10 Sep 2020 David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, Antonio Torralba

Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.

Image Classification Image Generation +1

Detecting natural disasters, damage, and incidents in the wild

1 code implementation ECCV 2020 Ethan Weber, Nuria Marzo, Dim P. Papadopoulos, Aritro Biswas, Agata Lapedriza, Ferda Ofli, Muhammad Imran, Antonio Torralba

While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.

Context Based Emotion Recognition using EMOTIC Dataset

3 code implementations30 Mar 2020 Ronak Kosti, Jose M. Alvarez, Adria Recasens, Agata Lapedriza

In this paper we present EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion.

Ranked #3 on Emotion Recognition in Context on EMOTIC (using extra training data)

Emotion Recognition in Context

Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog

no code implementations ICLR 2020 Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard

This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e. g. systems that learn from human interaction.

OpenAI Gym Open-Domain Dialog +3

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

1 code implementation30 Jun 2019 Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.

Open-Domain Dialog Q-Learning +2

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

2 code implementations NeurIPS 2019 Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Agata Lapedriza, Rosalind Picard

To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level.

Dialogue Evaluation Knowledge Distillation

Emotion Recognition in Context

no code implementations CVPR 2017 Ronak Kosti, Jose M. Alvarez, Adria Recasens, Agata Lapedriza

In this paper we present the Emotions in Context Database (EMCO), a dataset of images containing people in context in non-controlled environments.

Emotion Recognition in Context

Places: An Image Database for Deep Scene Understanding

no code implementations6 Oct 2016 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Antonio Torralba, Aude Oliva

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 recognition.

BIG-bench Machine Learning Classification +4

Learning Deep Features for Discriminative Localization

33 code implementations CVPR 2016 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba

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 localization ability despite being trained on image-level labels.

Weakly-Supervised Object Localization

Speeding Up Neural Networks for Large Scale Classification using WTA Hashing

no code implementations28 Apr 2015 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 networks.

General Classification

Object Detectors Emerge in Deep Scene CNNs

1 code implementation22 Dec 2014 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba

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 examples (e. g., ImageNet, Places), the state of the art in computer vision is advancing rapidly.

General Classification Object Localization +2

Learning Deep Features for Scene Recognition using Places Database

no code implementations NeurIPS 2014 Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva

Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success.

Object Recognition Scene Recognition

Are all training examples equally valuable?

no code implementations25 Nov 2013 Agata Lapedriza, Hamed Pirsiavash, Zoya Bylinskii, Antonio Torralba

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.

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