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Aude Oliva
Researcher at Massachusetts Institute of Technology
Publications - 245
Citations - 55991
Aude Oliva is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Scene statistics & Cognitive neuroscience of visual object recognition. The author has an hindex of 75, co-authored 238 publications receiving 45527 citations. Previous affiliations of Aude Oliva include University of Glasgow & Brigham and Women's Hospital.
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Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Proceedings ArticleDOI
Learning Deep Features for Discriminative Localization
TL;DR: This work revisits the global average pooling layer proposed in [13], and sheds light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on imagelevel labels.
Posted Content
Learning Deep Features for Discriminative Localization
TL;DR: In this article, the authors revisited the global average pooling layer and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.
Journal ArticleDOI
Places: A 10 Million Image Database for Scene Recognition
TL;DR: The Places Database is described, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world, using the state-of-the-art Convolutional Neural Networks as baselines, that significantly outperform the previous approaches.
Proceedings Article
Learning Deep Features for Scene Recognition using Places Database
TL;DR: A new scene-centric database called Places with over 7 million labeled pictures of scenes is introduced with new methods to compare the density and diversity of image datasets and it is shown that Places is as dense as other scene datasets and has more diversity.