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Sanja Fidler
Researcher at Nvidia
Publications - 286
Citations - 31272
Sanja Fidler is an academic researcher from Nvidia. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 71, co-authored 249 publications receiving 22241 citations. Previous affiliations of Sanja Fidler include University of Toronto & Toyota Technological Institute at Chicago.
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Proceedings ArticleDOI
Scene Parsing through ADE20K Dataset
TL;DR: The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
Proceedings ArticleDOI
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu,Ryan Kiros,Richard S. Zemel,Ruslan Salakhutdinov,Raquel Urtasun,Antonio Torralba,Sanja Fidler +6 more
TL;DR: The authors align books to their movie releases to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets, and propose a context-aware CNN to combine information from multiple sources.
Proceedings Article
Skip-thought vectors
Ryan Kiros,Yukun Zhu,Ruslan Salakhutdinov,Richard S. Zemel,Antonio Torralba,Raquel Urtasun,Sanja Fidler +6 more
TL;DR: This article used the continuity of text from books to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage, which can produce highly generic sentence representations that are robust and perform well in practice.
Proceedings ArticleDOI
The Role of Context for Object Detection and Semantic Segmentation in the Wild
Roozbeh Mottaghi,Xianjie Chen,Xiaobai Liu,Nam-Gyu Cho,Seong-Whan Lee,Sanja Fidler,Raquel Urtasun,Alan L. Yuille +7 more
TL;DR: A novel deformable part-based model is proposed, which exploits both local context around each candidate detection as well as global context at the level of the scene, which significantly helps in detecting objects at all scales.
Posted Content
Skip-Thought Vectors
Ryan Kiros,Yukun Zhu,Ruslan Salakhutdinov,Richard S. Zemel,Antonio Torralba,Raquel Urtasun,Sanja Fidler +6 more
TL;DR: The approach for unsupervised learning of a generic, distributed sentence encoder is described, using the continuity of text from books to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage.