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 Spring 2013
All talks take place Thursdays at 4:30pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| January 24 | (no meeting) | |
| January 31 | Mohammad Rastegari | Scalable object-class retrieval with approximate and top-k ranking |
| February 7 | Angjoo Kanazawa | Dog Breed Classification Using Part Localization |
| February 14 | Stephen Xi Chen | Piecing Together the Segmentation Jigsaw using Context |
| February 21 | Guangxiao Zhang | Discriminative Dictionary Learning for Sparse Coding : A Batch Version and a Semi-Supervised Online Version |
| February 28 | Kota Hara | |
| March 7 | Prof. Mohand Said Allili | Statistical Multi-Scale Decomposition Modeling of Texture and Applications |
| March 14 | Arijit Biswas | |
| March 21 | (Spring Break, no meeting) | |
| March 28 | (Midterms, no meeting) | |
| April 4 | ||
| April 11 | (ICCV deadline, no meeting) | |
| April 18 | Raviteja Vemulapalli | |
| April 25 | ||
| May 2 | Xavier Gibert Serra |
Talk Abstracts Spring 2013
Scalable object-class retrieval with approximate and top-k ranking
Speaker: Mohammad Rastegari -- Date: January 31, 2013
In this paper we address the problem of object-class retrieval in large image data sets: given a small set of training examples defining a visual category, the objective is to efficiently retrieve images of the same class from a large database. We propose two contrasting retrieval schemes achieving good accuracy and high efficiency. The first exploits sparse classification models expressed as linear combinations of a small number of features. These sparse models can be efficiently evaluated using inverted file indexing. Furthermore, we introduce a novel ranking procedure that provides a significant speedup over inverted file indexing when the goal is restricted to finding the top-k (i.e., the k highest ranked) images in the data set. We contrast these sparse retrieval models with a second scheme based on approximate ranking using vector quantization. Experimental results show that our algorithms for object-class retrieval can search a 10 million database in just a couple of seconds and produce categorization accuracy comparable to the best known class-recognition systems.
Dog Breed Classification Using Part Localization
Speaker: Angjoo Kanazawa -- Date: February 7, 2013
We propose a novel approach to fine-grained image classification in which instances from different classes share common parts but have wide variation in shape and appearance. We use dog breed identification as a test case to show that extracting corresponding parts improves classification performance. This domain is especially challenging since the appearance of corresponding parts can vary dramatically, e.g., the faces of bulldogs and beagles are very different. To find accurate correspondences, we build exemplar-based geometric and appearance models of dog breeds and their face parts. Part correspondence allows us to extract and compare descriptors in like image locations. Our approach also features a hierarchy of parts (e.g., face and eyes) and breed-specific part localization. We achieve 67% recognition rate on a large real-world dataset including 133 dog breeds and 8,351 images, and experimental results show that accurate part localization significantly increases classification performance compared to state-of-the-art approaches.
Piecing Together the Segmentation Jigsaw using Context
Speaker: Stephen Xi Chen -- Date: February 14, 2013
We present an approach to jointly solve the segmentation and recognition problem using a multiple segmentation framework. We formulate the problem as segment selection from a pool of segments, 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. Our approach improves labeling performance compared to other segmentation based recognition approaches.
Discriminative Dictionary Learning for Sparse Coding : A Batch Version and a Semi-Supervised Online Version
Speaker: Guangxiao Zhang -- Date: February 21, 2013
Dictionary learning has been a hot topic in recent years in computer vision. Many dictionary learning strategies have been proposed and led to excellent results in image denoising, inpainting, and recognition. However, most of them optimizes for reconstruction and therefore may not be the best for classification. Moreover, such dictionaries are often over-complete (more dictionary items than the dimension), which is computationally costly when the number of categories is large.
In the first part of the talk, we present a greedy learning algorithm to obtain a compact (small-sized) and discriminative dictionary. Starting with an over-complete dictionary, we map the dictionary items with label into an undirected k-nearest neighbor graph, and model the discriminative dictionary learning as a graph topology selection problem. By optimizing a monotonic, submodular objective function, our algorithm is shown to be highly efficient and effective in face recognition, object recognition, and action/gesture classification tasks.
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
Past Semesters
Funded By
- Computer Vision Faculty
- Northrop Grumman
Current Seminar Series Coordinators
Emails are at umiacs.umd.edu.
| Ejaz Ahmed, ejaz@ | (student of Professor Larry Davis) |
| Angjoo Kanazawa, kanazawa@ | (student of Professor David Jacobs) |
| Jie Ni, jni@ | (student of Professor Rama Chellappa) |
| Ching Lik Teo, cteo@ | (student of Professor Yiannis Aloimonos) |
Gone but not forgotten.
| Anne Jorstad | now PostDoc at EPFL |
| Sameh Khamis | off this semester |
| Sima Taheri |