Discovering states and transformations in image collections
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state_cvpr15.pdf
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20.49 MB
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Author(s)
Isola, Phillip John
Lim, Joseph Jaewhan
Adelson, Edward H
Date Issued
October 2015
Journal
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Isola, Phillip et al. “Discovering States and Transformations in Image Collections.” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12 2015, Boston, Massachusetts, USA, Institute of Electrical and Electronics Engineers (IEEE), October 2015 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Objects in visual scenes come in a rich variety of transformed states. A few classes of transformation have been heavily studied in computer vision: mostly simple, parametric changes in color and geometry. However, transformations in the physical world occur in many more flavors, and they come with semantic meaning: e.g., bending, folding, aging, etc. The transformations an object can undergo tell us about its physical and functional properties. In this paper, we introduce a dataset of objects, scenes, and materials, each of which is found in a variety of transformed states. Given a novel collection of images, we show how to explain the collection in terms of the states and transformations it depicts. Our system works by generalizing across object classes: states and transformations learned on one set of objects are used to interpret the image collection for an entirely new object class.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CVPR.2015.7298744
