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Successful meeting thanks to all 250+ attendees!
(discussion in the morning section)
Overview
A recent theme throughout science is the advent of "big data" -- leveraging huge datasets to tackle fundamental problems. Computer Vision is also experiencing this paradigm shift, with large annotated image and video datasets becoming available to researchers. These have the potential to revolutionize everything from low level vision to object detection to scene understanding. But to realize this potential, researchers must address the fundamental computational problems inherent with such large datasets. Concurrently, Machine Learning researchers are exploring this "big data" paradigm, developing theory and techniques that can be applied to a range of data modalities. The goal of this workshop is to bring together leaders in the theory and practice of large scale machine learning with computer vision researchers who have begun to explore the challenge and promise of large amounts of data.- Identify large scale problems in computer vision
- Present large scale machine learning and optimization techniques, distinct from those used in computer vision
- Discuss new directions provided by the problems, data, and techniques from both sides
- Encourage vision researchers to expand their repertoire of approaches
- Remind machine learning researchers that vision is an exciting problem and to encourage work that benefits our community!
| 8:00-9:00 | Breakfast Buffet | |
| 9:00-9:40 | Keynote David Forsyth University of Illinois at Urbana-Champain | More Words and Bigger Pictures: Where could large-scale learning take us? |
| 9:40-10:20 | Keynote Eric Xing Carnegie Mellon University | Sparsity and Learning Large Scale Models |
| 10:20-10:40 | Coffee/Snacks | |
| 10:40-11:20 | Keynote Jason Weston Google Incorporated | Large Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings |
| 11:20-11:40 | Fei-Fei Li Stanford University | Building the Forest: Large Scale Data and Modeling in Computer Vision |
| 11:40-12:00 | Discussion ( All ) | |
| 12:00-1:20 | Lunch Buffet | |
| 1:20-1:40 | James Hays Brown University | Scene understanding and representation at Internet-scale. |
| 1:40-2:00 | Kristen Grauman University of Texas at Austin | Live Large-Scale Active Learning |
| 2:00-2:20 | Svetlana Lazebnik University of North Carolina, Chapel Hill | Large-scale nonparametric image parsing |
| 2:20-2:50 | Discussion ( All ) | |
| 2:50-3:00 | Prizes | Most Controversial, Most Questions, etc. |
| 3:00-3:40 | Coffee/Snacks |