310 citations
- École Polytechnique Fédérale de LausanneCH87 papers
- Institut Denis PoissonFR42 papers
- Université de ToursFR37 papers
- Université d'OrléansFR37 papers
- Centre National de la Recherche ScientifiqueFR10 papers
- Brno University of TechnologyCZ7 papers
- CERMICSFR7 papers
- University of ManchesterGB7 papers
- Italian Institute of TechnologyIT6 papers
- Queen Mary University of LondonGB6 papers
- University of BernCH6 papers
- Institute for Scientific and Technological ResearchMX5 papers
8 papers · 1 filter
Characterizing the Demographics Behind the #BlackLivesMatter Movement
Alexandra Olteanu, Ingmar Weber, Daniel Gatica-Perez
The debates on minority issues are often dominated by or held among the concerned minority: gender equality debates have often failed to engage men, while those about race fail to…
Principled Parallel Mean-Field Inference for Discrete Random Fields
Pierre Baqué, Timur Bagautdinov, François Fleuret +1
Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and i…
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Brendan Jou, Tao Chen, Nikolaos Pappas +3
Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics…
CommuniSense: Crowdsourcing Road Hazards in Nairobi
Darshan Santani, Jidraph Njuguna, Tierra Bills +4
Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obta…
A Deeper Look at Dataset Bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo +1
The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning…
Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation
Faraz Saeedan, Barbara Caputo
As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful co…