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 Fall 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
 
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| September 6
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| December 3
 
| Angjoo Kanazawa
 
| Angjoo Kanazawa
−
| Face Alignment by Explicit Shape Regression
+
| Learning 3D Deformation of Animals from 2D Images
−
|-
 
−
| September 13
 
−
| Sameh Khamis
 
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| Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition
 
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|-
 
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| September 20
 
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| Douglas Summerstay
 
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|
 
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| September 27
 
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| Mohammad Rastegari
 
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|
 
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|-
 
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| October 4
 
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|
 
−
|
 
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|-
 
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| October 11
 
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| ''(ECCV week, no meeting)''
 
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|
 
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|-
 
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| October 18
 
−
|
 
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|
 
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|-
 
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| October 25
 
−
| Yi-Chen Chen
 
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|
 
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|-
 
−
| November 1
 
−
|
 
−
|
 
−
|-
 
−
| November 8
 
−
|
 
−
|
 
−
|-
 
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| November 15
 
−
| Ang Li
 
−
|
 
−
|-
 
−
| November 22
 
−
| Arijit Biswas
 
−
|
 
−
|-
 
−
| November 29
 
−
| Fatemeh Mir Rashed
 
−
|
 
−
|-
 
−
| December 6
 
−
| Ejaz Ahmed
 
−
|
 
 
|-
 
|-
−
| December 13
+
| December 10
−
| ''(Final exams, no meeting)''
+
| Xintong Han
−
|
+
| Automated Event Retrieval using Web Trained Detectors
 
|}
 
|}
 +
 +
==Talk Abstracts Spring 2015==
  
  
−
==Talk Abstracts Fall 2012==
+
===Learning 3D Deformation of Animals from 2D Images===
 +
Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: December 3, 2015
  
−
===Face Alignment by Explicit Shape Regression===
+
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.
−
Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: September 6, 2012
 
  
−
In this talk, we will go over CVPR 2012 paper "Face Alignment by Explicit Shape Regression". I will review the paper and discuss its key concepts: cascaded regression, random ferns, shape indexed image features, and correlation based feature selection. Then I will discuss our hypothesis on why this seemingly simple method works so well and how we can apply their method to similar problem domains such as dog and bird parts localization and their challenges.
+
Link: [http://arxiv.org/pdf/1507.07646v1.pdf paper]
  
−
Abstract from the paper:
+
===Automated Event Retrieval using Web Trained Detectors===
−
We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.
 
  
 +
Speaker: [http://www.umiacs.umd.edu/~xintong/ 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: [http://arxiv.org/pdf/1509.07845v1.pdf paper]
  
 
==Past Semesters==
 
==Past Semesters==
−
* [[cvss_spring2012|Schedule Spring 2012]]
+
* [[Cvss:Spring2015| Spring 2015]]
−
* [[cvss_fall2011|Schedule Fall 2011]]
+
* [[cvss fall2014|Fall 2014]]
−
* [[cvss_summer2011|Schedule Summer 2011]]
+
* [[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 114: 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@
 
| [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa], kanazawa@
−
| (student of [http://www.cs.umd.edu/~djacobs/ Professor David Jacobs])
+
| (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])
 
|-
 
|-
−
| [http://www.umiacs.umd.edu/~sameh/ Sameh Khamis], sameh@
+
| [http://www.umiacs.umd.edu/~xintong/ Xintong Han], xintong@
 
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| [http://www.umiacs.umd.edu/~jni/ Jie Ni], jni@
+
| [http://www.cs.umd.edu/~bharat/ Bharat Singh], bharat@
−
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
+
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| [http://www.umiacs.umd.edu/~cteo/ Ching Lik Teo], cteo@
+
| [http://bcsiriuschen.github.io/ Bor-Chun (Sirius) Chen], sirius@
−
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos])
+
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|}
 
|}
  
 
Gone but not forgotten.
 
Gone but not forgotten.
−
 
+
{| cellpadding="1"
−
{| class="wikitable" cellpadding="5"
+
|-
 +
| [http://www.umiacs.umd.edu/~jhchoi/ Jonghyun Choi], jhchoi@
 +
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| [http://www-users.math.umd.edu/~jorstad/ Anne Jorstad], jorstad@
+
| Ching-Hui Chen, ching@
−
| (student of [http://www.cs.umd.edu/~djacobs/ Professor David Jacobs])
+
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
 +
|
 
|-
 
|-
−
| [http://www.umiacs.umd.edu/~taheri/ Sima Taheri], taheri@
+
| [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])
 +
|-
 +
| [http://www.umiacs.umd.edu/~sameh/ Sameh Khamis]
 +
|
 +
|-
 +
| [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"