Difference between revisions of "Main Page"

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* 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.
 
* 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 2015==
  
−
==Schedule Spring 2012==
+
All talks take place on Thursdays at 3:30pm in AVW 3450.
  
−
All talks take place Thursdays at 4:30pm in AVW 3450.
+
{| class="wikitable" cellpadding="10" border="1" cellspacing="1"
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−
{| class="wikitable" cellpadding="10" border="1" cellspacing="0"
 
 
|-
 
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! Date
 
! Date
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! Title
 
! Title
 
|-
 
|-
−
| February 2
+
| December 3
−
| Ching Lik Teo
+
| Angjoo Kanazawa
−
| The Telluride Neuromorphic Workshop Experience
+
| Learning 3D Deformation of Animals from 2D Images
−
|-
 
−
| February 9
 
−
| Jonghyun Choi
 
−
| A Complementary Local Feature Descriptor for Face Identification (CCS-POP)
 
−
|-
 
−
| February 16
 
−
| Sameh Khamis
 
−
| Energy Minimization with Graph Cuts
 
−
|-
 
−
| February 23
 
−
| Jay Pujara
 
−
| Using Classifier Cascades for Scalable E-Mail Classification
 
−
|-
 
−
| March 1
 
−
| ''(ECCV week, no meeting)''
 
−
|
 
−
|-
 
−
| March 8
 
−
| Jie Ni
 
−
| Example-Driven Manifold Priors for Image Deconvolution
 
−
|-
 
−
| March 15
 
−
| Huimin Guo
 
−
|
 
−
|-
 
−
| March 22
 
−
| ''(Spring Break)''
 
−
|  
 
 
|-
 
|-
−
| March 29
+
| December 10
−
| Daozheng Chen
+
| Xintong Han
−
|
+
| Automated Event Retrieval using Web Trained Detectors
−
|-
 
−
| April 5
 
−
| Jaishanker Pillai
 
−
|
 
−
|-
 
−
| April 12
 
−
| Jun-Cheng Chen
 
−
|
 
−
|-
 
−
| April 19
 
−
| Sima Taheri
 
−
|
 
−
|-
 
−
| April 26
 
−
| Sujal Bista
 
−
|
 
−
|-
 
−
| May 3
 
−
| Nazre Batool
 
−
|
 
−
|-
 
−
| May 10
 
−
| Stephen Xi Chen
 
−
|
 
−
|-
 
−
| May 17
 
−
|''(no meeting, final exams)''
 
−
|
 
 
|}
 
|}
  
 +
==Talk Abstracts Spring 2015==
  
−
==Talk Abstracts Spring 2012==
 
−
 
−
===The Telluride Neuromorphic Workshop Experience===
 
−
Speaker: [http://www.umiacs.umd.edu/~cteo/ Ching Lik Teo] -- Date: February 2, 2012
 
−
 
−
In this talk, I will present what we did as a group at the Telluride Neuromorphic Workshop 2011. I will explain the challenges we faced, modules that we have used, and some results from experiments on activity description we have conducted on the robot.
 
−
 
−
===A Complementary Local Feature Descriptor for Face Identification (CCS-POP)===
 
−
Speaker: [http://umiacs.umd.edu/~jhchoi/ Jonghyun Choi] -- Date: February 9, 2012
 
−
 
−
In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric-Pairs of Pixels (CCS-POP). It concatenates the symmetric pixel differences centered at a pixel position along various orientations with various radii; it is a generalized form of Local Binary Patterns, its variants and Pairs-of-Pixels (POP). Combining CCS-POP with existing descriptors achieves better face identification performance on FRGC Ver. 1.0 and FERET datasets compared to state-of-the-art approaches.
 
  
−
===Energy Minimization with Graph Cuts===
+
===Learning 3D Deformation of Animals from 2D Images===
−
Speaker: [http://www.umiacs.umd.edu/~sameh/ Sameh Khamis] -- Date: February 16, 2012
+
Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: December 3, 2015
  
−
In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions.
+
Abstract: Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user-clicked images. Our framework is based on a novel locally-bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user-clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.
  
−
===Using Classifier Cascades for Scalable E-Mail Classification===
+
Link: [http://arxiv.org/pdf/1507.07646v1.pdf paper]
−
Speaker: [http://www.cs.umd.edu/~jay/ Jay Pujara] -- Date: February 23, 2012
 
  
−
In many real-world scenarios, we must make judgments in the presence of computational constraints. One common computational constraint arises when the features used to make a judgment each have differing acquisition costs, but there is a fixed total budget for a set of judgments. Particularly when there are a large number of classifications that must be made in a real-time, an intelligent strategy for optimizing accuracy versus computational costs is essential. E-mail classification is an area where accurate and timely results require such a trade-off. We identify two scenarios where intelligent feature acquisition can improve classifier performance. In granular classification we seek to classify e-mails with increasingly specific labels structured in a hierarchy, where each level of the hierarchy requires a different trade-off between cost and accuracy. In load-sensitive classification, we classify a set of instances within an arbitrary total budget for acquiring features. Our method, Adaptive Classifier Cascades (ACC), designs a policy to combine a series of base classifiers with increasing computational costs given a desired trade-off between cost and accuracy. Using this method, we learn a relationship between feature costs and label hierarchies, for granular classification and cost budgets, for load-sensitive classification. We evaluate our method on real-world e-mail datasets with realistic estimates of feature acquisition cost, and we demonstrate superior results when compared to baseline classifiers that do not have a granular, cost-sensitive feature acquisition policy.
+
===Automated Event Retrieval using Web Trained Detectors===
  
−
===Example-Driven Manifold Priors for Image Deconvolution===
+
Speaker: [http://www.umiacs.umd.edu/~xintong/ Xintong Han] -- Date: December 10, 2015
−
Speaker: Jie Ni -- Date: March 8, 2012
 
  
−
Image restoration methods that exploit prior information about images to be estimated have been extensively studied, typically using the Bayesian framework. In this work, we consider the role of prior knowledge of the object class in the form of a patch manifold to address the deconvolution problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold prior is implicitly estimated from the given unlabeled data. We show how the patch-manifold prior effectively exploits the available sample class data for regularizing the  econvolution problem. Furthermore, we derive a generalized cross-validation (GCV) function to automatically determine the regularization parameter at each iteration without explicitly knowing the noise variance. Extensive experiments show that this method performs better than many competitive image deconvolution methods.
+
Abstract: Complex event retrieval is a challenging research problem, especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos. However, defining an exhaustive concept lexicon and pre-training it requires vast computational resources. Therefore, recent approaches automate concept discovery and training by leveraging large amounts of weakly annotated web data. Compact visually salient concepts are automatically obtained by the use of concept pairs or, more generally, n-grams. However, not all visually salient n-grams are necessarily useful for an event query - some combinations of concepts may be visually compact but irrelevant--and this drastically affects performance. We propose an event retrieval algorithm that constructs pairs of automatically discovered concepts and then prunes those concepts that are unlikely to be helpful for retrieval. Pruning depends both on the query and on the specific video instance being evaluated. Our approach also addresses calibration and domain adaptation issues that arise when applying concept detectors to unseen videos. We demonstrate large improvements over other vision based systems on the TRECVID MED 13 dataset.
  
 +
Link: [http://arxiv.org/pdf/1509.07845v1.pdf paper]
  
 
==Past Semesters==
 
==Past Semesters==
−
* [[cvss_fall2011|Schedule Fall 2011]]
+
* [[Cvss:Spring2015| Spring 2015]]
−
* [[cvss_summer2011|Schedule Summer 2011]]
+
* [[cvss fall2014|Fall 2014]]
 +
* [[cvss_spring2014|Spring 2014]]
 +
* [[cvss_fall2013|Fall 2013]]
 +
* [[cvss_summer2013|Summer 2013]]
 +
* [[cvss_spring2013|Spring 2013]]
 +
* [[cvss_fall2012|Fall 2012]]
 +
* [[cvss_spring2012|Spring 2012]]
 +
* [[cvss_fall2011|Fall 2011]]
 +
* [[cvss_summer2011|Summer 2011]]
  
 +
==Funded By==
 +
* Computer Vision Faculty
 +
<!-- * '''[http://www.northropgrumman.com/ Northrop Grumman]''' -->
  
 
==Current Seminar Series Coordinators==
 
==Current Seminar Series Coordinators==
Line 134: Line 77:
 
Emails are at umiacs.umd.edu.
 
Emails are at umiacs.umd.edu.
  
−
{| class="wikitable" cellpadding="5"
+
{| cellpadding="1"
 +
|-
 +
| [http://sites.google.com/site/austinomyers/ Austin Myers], amyers@
 +
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos])
 +
|-
 +
| [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa], kanazawa@
 +
| (student of [http://cs.umd.edu/~djacobs/ Professor David Jacobs])
 +
|-
 +
| [http://sites.google.com/site/yechengxi/ Chenxi Ye] cxy@
 +
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos])
 
|-
 
|-
−
| Anne Jorstad, jorstad@
+
| [http://www.umiacs.umd.edu/~xintong/ Xintong Han], xintong@
−
| (student of [http://www.cs.umd.edu/~djacobs/ Professor David Jacobs])
+
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| Sameh Khamis, sameh@
+
| [http://www.cs.umd.edu/~bharat/ Bharat Singh], bharat@
 
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| Sima Taheri, taheri@
+
| [http://bcsiriuschen.github.io/ Bor-Chun (Sirius) Chen], sirius@
 +
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 +
|}
 +
 
 +
Gone but not forgotten.
 +
{| cellpadding="1"
 +
|-
 +
| [http://www.umiacs.umd.edu/~jhchoi/ Jonghyun Choi], jhchoi@
 +
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 +
|-
 +
| Ching-Hui Chen, ching@
 +
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
 +
|
 +
|-
 +
| [http://ravitejav.weebly.com/ Raviteja Vemulapalli], raviteja @
 
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
 
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
 
|-
 
|-
−
| Ching Lik Teo, cteo@
+
| [http://www.umiacs.umd.edu/~sameh/ Sameh Khamis]
−
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos])
+
|
 +
|-
 +
| [http://www.umiacs.umd.edu/~ejaz/ Ejaz Ahmed]
 +
|
 +
|-
 +
| [http://cvlabwww.epfl.ch/~jorstad/ Anne Jorstad]
 +
| now at EPFL
 +
|-
 +
| [http://www.umiacs.umd.edu/~jni/ Jie Ni]
 +
| now at Sony
 +
|-
 +
| [http://www.umiacs.umd.edu/~taheri/ Sima Taheri]
 +
|
 +
|-
 +
| [http://www.umiacs.umd.edu/~cteo/ Ching Lik Teo]
 +
|
 
|}
 
|}

Latest revision as of 23:40, 3 December 2015

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 2015[edit]

All talks take place on Thursdays at 3:30pm in AVW 3450.

Date Speaker Title
December 3 Angjoo Kanazawa Learning 3D Deformation of Animals from 2D Images
December 10 Xintong Han Automated Event Retrieval using Web Trained Detectors

Talk Abstracts Spring 2015[edit]

Learning 3D Deformation of Animals from 2D Images[edit]

Speaker: Angjoo Kanazawa -- Date: December 3, 2015

Abstract: Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user-clicked images. Our framework is based on a novel locally-bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user-clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.

Link: paper

Automated Event Retrieval using Web Trained Detectors[edit]

Speaker: Xintong Han -- Date: December 10, 2015

Abstract: Complex event retrieval is a challenging research problem, especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos. However, defining an exhaustive concept lexicon and pre-training it requires vast computational resources. Therefore, recent approaches automate concept discovery and training by leveraging large amounts of weakly annotated web data. Compact visually salient concepts are automatically obtained by the use of concept pairs or, more generally, n-grams. However, not all visually salient n-grams are necessarily useful for an event query - some combinations of concepts may be visually compact but irrelevant--and this drastically affects performance. We propose an event retrieval algorithm that constructs pairs of automatically discovered concepts and then prunes those concepts that are unlikely to be helpful for retrieval. Pruning depends both on the query and on the specific video instance being evaluated. Our approach also addresses calibration and domain adaptation issues that arise when applying concept detectors to unseen videos. We demonstrate large improvements over other vision based systems on the TRECVID MED 13 dataset.

Link: paper

Past Semesters[edit]

Funded By[edit]

  • Computer Vision Faculty

Current Seminar Series Coordinators[edit]

Emails are at umiacs.umd.edu.

Austin Myers, amyers@ (student of Professor Yiannis Aloimonos)
Angjoo Kanazawa, kanazawa@ (student of Professor David Jacobs)
Chenxi Ye cxy@ (student of Professor Yiannis Aloimonos)
Xintong Han, xintong@ (student of Professor Larry Davis)
Bharat Singh, bharat@ (student of Professor Larry Davis)
Bor-Chun (Sirius) Chen, sirius@ (student of Professor Larry Davis)

Gone but not forgotten.

Jonghyun Choi, jhchoi@ (student of Professor Larry Davis)
Ching-Hui Chen, ching@ (student of Professor Rama Chellappa)
Raviteja Vemulapalli, raviteja @ (student of Professor Rama Chellappa)
Sameh Khamis
Ejaz Ahmed
Anne Jorstad now at EPFL
Jie Ni now at Sony
Sima Taheri
Ching Lik Teo

Web Accessibility

Retrieved from "https://wiki.cs.umd.edu/cvss/w/index.php?title=Main_Page&oldid=1868"