The document discusses improving relations between academia and industry, specifically Microsoft and academia. It describes Microsoft's work in computer vision and the process of transferring technology from research to products. It also outlines Microsoft's programs for collaborating with academia. Finally, it proposes ways to strengthen relations such as improving access to tutorials and libraries, researcher training, and increasing information sharing between industry and academia.

1. Relations between
Academia and Industry
Speaker: Rick Szeliski
Organizer: David Lowe
Wednesday, August 24th

2. Computer Vision at Microsoft
• Photo editing (stitching, PhotoFuse, GrabCut)
• Photo Tourism → Photosynth
• Maps: photogrammetry, stitching
• Mobile recognition: product search, OCR
• Mobile (computational) photography
• Kinect
• Medical image analysis (Amalga)

3. Tech transfer at Microsoft
“Classic” 3-stage push model:
1. Research papers (stitching, PhotoMontage, Grab
Cut, Photo Tourism)
2. Prototype or incubation (ICE, GroupShot,
Photosynth, Lincoln)
3. Product
But also works other way (product pull):
– Kinect (secret project, hand-selected researchers)
– Amalga medical image analysis

4. Microsoft - Academia
• Microsoft Research Connections
• Microsoft Research Faculty Fellows
• Microsoft Research PhD Fellows
• Internships
• Faculty Summit

5. Improving relations (I)
• More accessible tutorials / teaching materials for
non-researchers:
– tutorials at conferences (will people attend?)
– on-line courses, exercises
• Better libraries:
– standard libraries (like OpenGL)
– free, non-commercial, commercial licenses
• Researcher training:
– efficient algorithms & coding (software engr.)
– scenario-driven research
– technical communications

6. Improving relations (II)
• More information flow industry → academia
– panels at conferences
– David’s list of computer vision companies
• encourage groups to list of areas and open problems,
e.g., http://www.disneyresearch.com/research/index.htm
• Funding models and IP
– tough one: lots of models, contracts vs. open gifts
– fellowships (few), internships (many)
– IP tricky both ways