T
Trevor Darrell
Researcher at University of California, Berkeley
Publications - 734
Citations - 222973
Trevor Darrell is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 148, co-authored 678 publications receiving 181113 citations. Previous affiliations of Trevor Darrell include Massachusetts Institute of Technology & Boston University.
Papers
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Proceedings ArticleDOI
Recognizing Image Style.
Sergey Karayev,Matthew Trentacoste,Helen Han,Aseem Agarwala,Trevor Darrell,Aaron Hertzmann,Holger Winnemoeller +6 more
TL;DR: An approach to predicting style of images, and a thorough evaluation of different image features for these tasks, find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels.
Patent
Three dimensional object pose estimation which employs dense depth information
Michele Covell,Michael Hongmai Lin,Ali Rahimi,Michael Harville,Trevor Darrell,John Iselin Woodfill,Harlyn Baker,G. Gordon +7 more
TL;DR: In this article, a model of connected patches is used to estimate the pose of an articulated figure, where each patch is the planar convex hull of two circles and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data.
Posted Content
LSDA: Large Scale Detection Through Adaptation
Judy Hoffman,Sergio Guadarrama,Eric Tzeng,Ronghang Hu,Jeff Donahue,Ross Girshick,Trevor Darrell,Kate Saenko +7 more
TL;DR: This paper proposes Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors.
Proceedings Article
Curiosity-driven Exploration by Self-supervised Prediction
TL;DR: In this paper, the authors formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model.
Proceedings ArticleDOI
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Lisa Anne Hendricks,Subhashini Venugopalan,Marcus Rohrbach,Raymond J. Mooney,Kate Saenko,Trevor Darrell +5 more
TL;DR: The Deep Compositional Captioner (DCC) is proposed to address the task of generating descriptions of novel objects which are not present in paired imagesentence datasets by leveraging large object recognition datasets and external text corpora and by transferring knowledge between semantically similar concepts.