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Computer Vision Student Seminars
The Computer Vision Student Seminars at the University of Maryland College Park are a student-run series of talks given by current graduate students for current graduate students.
To receive regular information about the Computer Vision Student Seminars, subscribe to our mailing list or our talks list.
Description[edit]
The purpose of these talks is to:
- Encourage interaction between computer vision students;
- Provide an opportunity for computer vision students to be aware of and possibly get involved in the research their peers are conducting;
- Provide an opportunity for computer vision students to receive feedback on their current research;
- Provide speaking opportunities for computer vision students.
The guidelines for the format are:
- An hour-long weekly meeting, consisting of one 20-40 minute talk followed by discussion and food.
- The talks are meant to be casual and discussion is encouraged.
- Topics may include current research, past research, general topic presentations, paper summaries and critiques, or anything else beneficial to the computer vision graduate student community.
Schedule Fall 2013[edit]
All talks take place on Thursdays at 4:30pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| September 19 | Mohammad Rastegari | TBA |
| September 26 | TBA | TBA |
| October 3 | TBA | TBA |
| October 10 | TBA | TBA |
| October 17 | TBA | TBA |
| October 24 | (CVPR deadline, no meeting) | |
| October 31 | (CVPR deadline, no meeting) | |
| November 7 | TBA | TBA |
| November 14 | Kota Hara | TBA |
| November 21 | Arunkumar Mohananchettiar | TBA |
| November 28 | Sumit Shekhar | TBA |
| December 5 | Arijit Biswas | TBA |
Talk Abstracts Fall 2013[edit]
Fast Image Prior[edit]
Speaker: Mohammad Rastegari -- Date: September 19, 2013
In this project we introduce a new method for learning image prior that can be used for many applications in image reconstruction. We learn a generative model on natural image patches. Our generative model is similar to one in Gausian Mixture Model (GMM). The key idea of our approach is to force each component of our generative model to share the same set of basis vectors. This leads to a much faster inference at test time. We used image denoising as our test bed for this image prior learning. Our experimental results shows that we reached about 30x speed up over state-of-the-art method while getting slightly improvement in denoising accuracy.
Past Semesters[edit]
Funded By[edit]
- Computer Vision Faculty
- Northrop Grumman
Current Seminar Series Coordinators[edit]
Emails are at umiacs.umd.edu.
| Angjoo Kanazawa, kanazawa@ | (student of Professor David Jacobs) |
| Sameh Khamis, sameh@ | (student of Professor Larry Davis) |
| Austin Myers, amyers@ | (student of Professor Yiannis Aloimonos) |
| Raviteja Vemulapalli, raviteja @ | (student of Professor Rama Chellappa) |
Gone but not forgotten.
| Ejaz Ahmed | |
| Anne Jorstad | now at EPFL |
| Jie Ni | off this semester |
| Sima Taheri | |
| Ching Lik Teo |
