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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 Spring 2014==
+
==Schedule Fall 2015==
  
 
All talks take place on Thursdays at 3:30pm in AVW 3450.
 
All talks take place on Thursdays at 3:30pm in AVW 3450.
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! Title
 
! Title
 
|-
 
|-
−
| January 30
+
| December 3
−
| Arpit Jain
+
| Angjoo Kanazawa
−
| Scene and Video Understanding
+
| Learning 3D Deformation of Animals from 2D Images
 
|-
 
|-
−
| February 6
+
| December 10
−
| Raviteja Vemulapalli
+
| Xintong Han
−
| Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
+
| Automated Event Retrieval using Web Trained Detectors
−
|-
 
−
| February 13
 
−
| ''Google DC PhD Summit, no meeting''
 
−
|
 
−
|-
 
−
| February 20
 
−
| TBA
 
−
| TBA
 
−
|-
 
−
| February 27
 
−
| Mohammad Rastegari
 
−
| TBA
 
−
|-
 
−
| March 6
 
−
| ''ECCV deadline, no meeting''
 
−
|
 
−
|-
 
−
| March 13
 
−
| Xavier Gibert Serra
 
−
| TBA
 
−
|-
 
−
| March 20
 
−
| ''Spring break, no meeting''
 
−
|
 
−
|-
 
−
| March 27
 
−
| Swaminathan Sankaranarayanan
 
−
| TBA
 
−
|-
 
−
| April 3
 
−
| Austin Myers
 
−
| TBA
 
−
|-
 
−
| April 10
 
−
| Jing Jing
 
−
| TBA
 
−
|-
 
−
| April 17
 
−
| Kota Hara
 
−
| TBA
 
−
|-
 
−
| April 24
 
−
| TBA
 
−
| TBA
 
−
|-
 
−
| May 1
 
−
| Garrett Warnell
 
−
| TBA
 
−
|-
 
−
| May 8
 
−
| TBA
 
−
| TBA
 
−
|-
 
−
| May 15
 
−
| Sumit Sekhar
 
−
| TBA
 
 
|}
 
|}
  
−
==Talk Abstracts Spring 2014==
+
==Talk Abstracts Spring 2015==
 +
 
  
−
===Scene and Video Understanding===
+
===Learning 3D Deformation of Animals from 2D Images===
−
Speaker: [http://www.umiacs.umd.edu/~ajain/ Arpit Jain] -- Date: January 30, 2014
+
Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: December 3, 2015
  
−
There has been significant improvements in the accuracy of scene understanding due to a shift from recognizing objects ``in isolation'' to context based recognition systems. Such systems improve recognition rates by augmenting appearance based models of individual objects with contextual information based on pairwise relationships between objects. These pairwise relations incorporate common world knowledge such as co-occurences and spatial arrangements of objects, scene layout, etc. However, these relations, even though consistent in 3D world, change due to viewpoint of the scene. In this thesis, we will look into the problems of incorporating contextual information from two different perspective for scene understanding problem (a)  ``what'' contextual relations are useful and ``how'' they should be incorporated into Markov network during inference. (b) jointly solve the segmentation and recognition problem using a multiple segmentation framework based on contextual information in conjunction with appearance matching. In the later part of the thesis, we will investigate different representations for video understanding and propose a discriminative patch based representation for videos.  
+
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.
  
−
Our work depart from traditional view of incorporating context into scene understanding problem where a fixed model for context is learned. We argue that context is scene dependent and propose a data-driven approach to predict the importance of edges and construct a Markov network for image analysis based on statistical models of global and local image features. Since all contextual information are not equally important, we also address the coupled problem of predicting the feature weights associated with each edge of a Markov network for evaluation of context. We then address the problem of fixed segmentation while modelling context by using a multiple segmentation framework and formulating the problem as ``a jigsaw puzzle''. We formulate the problem as segment selection from a pool of segments (jigsaws), assigning each selected segment a class label. Previous multiple segmentation approaches used local appearance matching to select segments in a greedy manner. In contrast, our approach formulates a cost function based on contextual information in conjunction with appearance matching. This relaxed cost function formulation is minimized using an efficient quadratic programming solver and an approximate solution is obtained by discretizing the relaxed solution.
+
Link: [http://arxiv.org/pdf/1507.07646v1.pdf paper]
  
−
Lastly, we propose a new representation for videos based on mid-level discriminative spatio-temporal patches. These spatio-temporal patches might correspond to a primitive human action, a semantic object, or perhaps a random but informative spatiotemporal patch in the video. What defines these spatiotemporal patches is their discriminative and representative properties. We automatically mine these patches from hundreds of training videos and experimentally demonstrate that these patches establish correspondence across videos and align the videos for label transfer techniques. Furthermore, these patches can be used as a discriminative vocabulary for action classification.
+
===Automated Event Retrieval using Web Trained Detectors===
  
−
===Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group===
+
Speaker: [http://www.umiacs.umd.edu/~xintong/ Xintong Han] -- Date: December 10, 2015
−
Speaker: [http://ravitejav.weebly.com/ Raviteja Vemulapalli] -- Date: February 6, 2014
 
  
−
Recently introduced cost-effective depth sensors coupled with the real-time skeleton estimation algorithm of Shotton et al. [16] have resulted in a renewed interest in skeleton-based human action recognition. Most of the earlier skeleton-based approaches used either the joint locations or the joint angles to represent a human skeleton. In this paper, we propose a new skeletal representation that explicitly models the 3D geometric relationships between various body parts using rotations and translations in 3D space. Since 3D rigid body motions are members of the special Euclidean group SE(3), the proposed skeletal representation lies in the Lie group SE(3)×. . .×SE(3), which is a curved manifold. With the proposed representation human actions can be modeled as curves in this Lie group. Since classification of curves in this Lie group is not an easy task, we map the action curves from the Lie group to its Lie algebra, which is a vector space. We then perform classification using a combination of dynamic time warping, Fourier temporal pyramid representation and linear SVM. Experimental results on three action datasets show that the proposed representation performs better than various other commonly-used skeletal representations. The proposed approach also outperforms various state-of-the-art skeleton-based human action recognition approaches.
+
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:Spring2015| Spring 2015]]
 +
* [[cvss fall2014|Fall 2014]]
 +
* [[cvss_spring2014|Spring 2014]]
 
* [[cvss_fall2013|Fall 2013]]
 
* [[cvss_fall2013|Fall 2013]]
 
* [[cvss_summer2013|Summer 2013]]
 
* [[cvss_summer2013|Summer 2013]]
Line 123: Line 71:
 
==Funded By==
 
==Funded By==
 
* Computer Vision Faculty
 
* Computer Vision Faculty
−
* '''[http://www.northropgrumman.com/ Northrop Grumman]'''
+
<!-- * '''[http://www.northropgrumman.com/ Northrop Grumman]''' -->
  
 
==Current Seminar Series Coordinators==
 
==Current Seminar Series Coordinators==
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{| cellpadding="1"
 
{| 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://www.umiacs.umd.edu/~sameh/ Sameh Khamis], sameh@
+
| [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/~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])
 
|-
 
|-
−
| [https://sites.google.com/site/austinomyers/ Austin Myers], amyers@
+
| [http://www.cs.umd.edu/~bharat/ Bharat Singh], bharat@
−
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos])
+
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|-
 
|-
−
| [http://ravitejav.weebly.com/ Raviteja Vemulapalli], raviteja @
+
| [http://bcsiriuschen.github.io/ Bor-Chun (Sirius) Chen], sirius@
−
| (student of [http://www.umiacs.umd.edu/~rama/ Professor Rama Chellappa])
+
| (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis])
 
|}
 
|}
  
 
Gone but not forgotten.
 
Gone but not forgotten.
−
 
 
{| cellpadding="1"
 
{| 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])
 +
|-
 +
| [http://www.umiacs.umd.edu/~sameh/ Sameh Khamis]
 +
|
 
|-
 
|-
 
| [http://www.umiacs.umd.edu/~ejaz/ Ejaz Ahmed]
 
| [http://www.umiacs.umd.edu/~ejaz/ Ejaz Ahmed]
Line 155: Line 121:
 
|-
 
|-
 
| [http://www.umiacs.umd.edu/~jni/ Jie Ni]
 
| [http://www.umiacs.umd.edu/~jni/ Jie Ni]
−
| off this semester
+
| now at Sony
 
|-
 
|-
 
| [http://www.umiacs.umd.edu/~taheri/ Sima Taheri]
 
| [http://www.umiacs.umd.edu/~taheri/ Sima Taheri]

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"