Difference between revisions of "Main Page"
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| − | === | + | ===Learning 3D Deformation of Animals from 2D Images=== |
Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: December 3, 2015 | Speaker: [http://www.umiacs.umd.edu/~kanazawa/ Angjoo Kanazawa] -- Date: December 3, 2015 | ||
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Link: [http://arxiv.org/pdf/1507.07646v1.pdf paper] | Link: [http://arxiv.org/pdf/1507.07646v1.pdf paper] | ||
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| + | ===Automated Event Retrieval using Web Trained Detectors=== | ||
| + | |||
| + | 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== | ||
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| − | | [http | + | | [http://sites.google.com/site/austinomyers/ Austin Myers], amyers@ |
| − | |||
| − | |||
| − | |||
| (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos]) | | (student of [http://www.cfar.umd.edu/~yiannis/ Professor Yiannis Aloimonos]) | ||
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| (student of [http://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.umiacs.umd.edu/~ | + | | (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]) | ||
| + | |- | ||
| + | | [http://www.cs.umd.edu/~bharat/ Bharat Singh], bharat@ | ||
| + | | (student of [http://www.umiacs.umd.edu/~lsd/ Professor Larry Davis]) | ||
| + | |- | ||
| + | | [http://bcsiriuschen.github.io/ Bor-Chun (Sirius) Chen], sirius@ | ||
| + | | (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 @ | | [http://ravitejav.weebly.com/ Raviteja Vemulapalli], raviteja @ | ||
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]
- Spring 2015
- Fall 2014
- Spring 2014
- Fall 2013
- Summer 2013
- Spring 2013
- Fall 2012
- Spring 2012
- Fall 2011
- Summer 2011
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 |
