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Andrew Owens
Researcher at University of Michigan
Publications - 53
Citations - 5723
Andrew Owens is an academic researcher from University of Michigan. The author has contributed to research in topics: Computer science & Tactile sensor. The author has an hindex of 22, co-authored 40 publications receiving 3819 citations. Previous affiliations of Andrew Owens include Massachusetts Institute of Technology & University of California, Berkeley.
Papers
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Proceedings ArticleDOI
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
TL;DR: SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places is introduced, and a generalization of bundle adjustment that incorporates object-to-object correspondences is introduced.
Book ChapterDOI
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Andrew Owens,Alexei A. Efros +1 more
TL;DR: In this paper, the authors argue that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation, and they propose to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned.
Proceedings ArticleDOI
CNN-Generated Images Are Surprisingly Easy to Spot… for Now
TL;DR: In this article, the authors show that a standard image classifier trained on only one specific CNN generator is able to generalize surprisingly well to unseen architectures, datasets, and training methods.
Book ChapterDOI
Ambient Sound Provides Supervision for Visual Learning
Andrew Owens,Jiajun Wu,Josh H. McDermott,William T. Freeman,William T. Freeman,Antonio Torralba +5 more
TL;DR: This work trains a convolutional neural network to predict a statistical summary of the sound associated with a video frame, and shows that this representation is comparable to that of other state-of-the-art unsupervised learning methods.
Journal ArticleDOI
SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion
TL;DR: This work presents an alternative formulation for SfM based on finding a coarse initial solution using a hybrid discrete-continuous optimization, and then improving that solution using bundle adjustment, and shows that it can produce models that are similar to or better than those produced with incremental bundles adjustment, but more robustly and in a fraction of the time.