cvss

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.

This summer CVSS will be held as a journal club on Thursdays at 4pm in AVW 4424.

Date	Discussion Leader	Title
July 3	Jason Filippou	Probabilistic Event Calculus based on Markov Logic Networks. Skarlatidis et al., RuleML '11 (Slides) Event Modeling and Recognition using Markov Logic Networks. Tran and Davis, ECCV '08 A Probabilistic Logic Programming Event Calculus. Skarlatidis et al., TPLP '14
July 11	David Shaw	Graphical Models. Jordan, StatSci '04 Nonparametric Belief Propagation. Sudderth et al., Commun. ACM '10
July 18	Sameh Khamis	Coupling Detection and Data Association for Multiple Object Tracking. Wu et al, CVPR '12 Discrete-Continuous Optimization for Multi-Target Tracking. Andriyenko et al, CVPR '12 Multi-target Tracking by Lagrangian Relaxation to Min-Cost Network Flow. Butt and Collins, CVPR '13
July 25	No Meeting
August 1	Raviteja Vemulapalli	Structured Learning and Prediction in Computer Vision. Nowozin and Lampert, now publishers '11 (Chapter 6) Kernelized Structural SVM Learning for Supervised Object Segmentation. Bertelli et al, CVPR '11
August 8	Varun Nagaraja	Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. Globerson and Jaakkola, NIPS '07 Tightening LP Relaxations for MAP using Message Passing. Sontag et al, UAI '08
August 15	Le Kang	Gradient-Based Learning Applied to Document Recognition. LeCun et al, IEEE '98 Tutorial on Implementing a Convolutional Neural Network with Theano.
August 22	Varun Manjunatha	Learning Deep Architectures for AI
August 29	TBA	TBA

Emails are at umiacs.umd.edu.

Angjoo Kanazawa, kanazawa@	(student of Professor David Jacobs)
Sameh Khamis, sameh@	(student of Professor Larry Davis)
Jie Ni, jni@	(student of Professor Rama Chellappa)
Ching Lik Teo, cteo@	(student of Professor Yiannis Aloimonos)

Gone but not forgotten.