Difference between revisions of "Main Page"
| Line 39: | Line 39: | ||
|- | |- | ||
| October 30 | | October 30 | ||
| − | | | + | | Cancelled |
| − | | | + | | Cancelled |
|- | |- | ||
| November 6 | | November 6 | ||
Revision as of 20:07, 28 October 2014
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 Spring 2014[edit]
All talks take place on Thursdays at 3:30pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| October 16 | Abhishek Sharma | Recursive Context Propagation Network for Semantic Scene Labeling |
| October 23 | Ang Li | Planar Structure Matching Under Projective Uncertainty for Geolocation |
| October 30 | Cancelled | Cancelled |
| November 6 | Ejaz Ahmed | |
| November 13 | CVPR deadline, no meeting | |
| November 20 | Kota Hara | |
| November 27 | Thanksgiving break, no meeting | |
| December 4 | Angjoo Kanazawa | |
| December 11 | Aleksandrs |
Talk Abstracts Fall 2014[edit]
Recursive Context Propagation Network for Semantic Scene Labeling[edit]
Speaker: Abhishek Sharma -- Date: October 16, 2014
Abstract: The talk will briefly touch upon the Multi-scale CNN of Lecun and Farabet to extract pixel-wise features for semantic segmentation and then I will move on to discuss the work we did to enhance the model further in order to result in a real-time and accurate pixel-wise labeling pipeline. I will talk about a deep feed-forward neural network architecture for pixel-wise semantic scene labeling. It uses a novel recursive neural network architecture for context propagation, referred to as rCPN. It first maps the local features into a semantic space followed by a bottom-up aggregation of local information into a global feature of the entire image. Then a top-down propagation of the aggregated information takes place that enhances the contextual information of each local features. Therefore, the information from every location in the image is propagated to every other location. Experimental results on Stanford background and SIFT Flow datasets show that the proposed method outperforms previous approaches in terms of accuracy. It is also orders of magnitude faster than previous methods and takes only 0.07 seconds on a GPU for pixel-wise labeling of a 256 by 256 image starting from raw RGB pixel values, given the super-pixel mask that takes an additional 0.3 seconds using an off-the-shelf implementation.
Planar Structure Matching Under Projective Uncertainty for Geolocation[edit]
Speaker: Ang Li -- Date: October 23, 2014
Abstract: Image based geolocation aims to answer the question: where was this ground photograph taken? We present an approach to geoloca- lating a single image based on matching human delineated line segments in the ground image to automatically detected line segments in ortho images. Our approach is based on distance transform matching. By ob- serving that the uncertainty of line segments is non-linearly amplified by projective transformations, we develop an uncertainty based repre- sentation and incorporate it into a geometric matching framework. We show that our approach is able to rule out a considerable portion of false candidate regions even in a database composed of geographic areas with similar visual appearances.
Past Semesters[edit]
Funded By[edit]
- Computer Vision Faculty
- Northrop Grumman
Current Seminar Series Coordinators[edit]
Emails are at umiacs.umd.edu.
| Jonghyun Choi, jhchoi@ | (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.
| Angjoo Kanazawa | |
| Sameh Khamis | |
| Ejaz Ahmed | |
| Anne Jorstad | now at EPFL |
| Jie Ni | off this semester |
| Sima Taheri | |
| Ching Lik Teo |
