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.
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Description
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 2012
All talks take place Thursdays at 4:30pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| September 6 | Angjoo Kanazawa | Face Alignment by Explicit Shape Regression |
| September 13 | Sameh Khamis | Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition |
| September 20 | Douglas Summerstay | |
| September 27 | Mohammad Rastegari | |
| October 4 | ||
| October 11 | (ECCV week, no meeting) | |
| October 18 | ||
| October 25 | Yi-Chen Chen | |
| November 1 | ||
| November 8 | ||
| November 15 | Ang Li | |
| November 22 | Arijit Biswas | |
| November 29 | Fatemeh Mir Rashed | |
| December 6 | Ejaz Ahmed | |
| December 13 | (Final exams, no meeting) |
Talk Abstracts Fall 2012
Face Alignment by Explicit Shape Regression
Speaker: 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.
Abstract from the paper: 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.
Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition
Speaker: Sameh Khamis -- Date: September 13, 2012
We propose a model to combine per-frame and per-track cues for action recognition. With multiple targets in a scene, our model simultaneously captures the natural harmony of an individual's action in a scene and the flow of actions of an individual in a video sequence, inferring valid tracks in the process. Our motivation is based on the unlikely discordance of an action in a structured scene, both at the track level (e.g., a person jogging then dancing) and the frame level (e.g., a person jogging in a dance studio). While we can utilize sampling approaches for inference in our model, we instead devise a global inference algorithm by decomposing the problem and solving the subproblems exactly and efficiently, recovering a globally optimal joint solution in several cases. Finally, we improve on the state-of-the-art action recognition results for two publicly available datasets.
Past Semesters
Current Seminar Series Coordinators
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.
| Anne Jorstad, jorstad@ | (student of Professor David Jacobs) |
| Sima Taheri, taheri@ | (student of Professor Rama Chellappa) |