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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 Fast Image Prior
September 26 (no meeting)
October 3 Abhishek Sharma A Sentence is Worth a Thousand Pixels
October 10 TBA TBA
October 17 Sumit Shekhar? TBA
October 24 (CVPR deadline, no meeting)
October 31 (CVPR deadline, no meeting)
November 7 Jingjing Zheng TBA
November 14 TBA TBA
November 21 Arunkumar Mohananchettiar TBA
November 28 (Thanksgiving, no meeting)
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.

A Sentence is Worth a Thousand Pixels[edit]

Speaker: Abhishek Sharma -- Date: October 3, 2013

We are interested in holistic scene understanding where images are accompanied with text in the form of complex sentential descriptions. We propose a holistic conditional random field model for semantic parsing which reasons jointly about which objects are present in the scene, their spatial extent as well as semantic segmentation, and employs text as well as image information as input. We automatically parse the sentences and extract objects and their relationships, and incorporate them into the model, both via potentials as well as by re-ranking candidate detections. We demonstrate the effectiveness of our approach in the challenging UIUC sentences dataset and show segmentation improvements of 12.5% over the visual only model and detection improvements of 5% AP over deformable part-based models.


Past Semesters[edit]

Funded By[edit]

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