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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.

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Description

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

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	Hyungtae Lee
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

Recursive Context Propagation Network for Semantic Scene Labeling

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.

N/A

Speaker: Ang Li

Abstract:

Past Semesters

• Spring 2014
• Fall 2013
• Summer 2013
• Spring 2013
• Fall 2012
• Spring 2012
• Fall 2011
• Summer 2011

Funded By

• Computer Vision Faculty
• Northrop Grumman

Current Seminar Series Coordinators

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

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