CSCI2951-B Data-Driven Vision and Graphics

Spring 2013, MWF 11:00 to 11:50, CIT 506.
Instructor: James Hays

Course Description

Course Catalog Entry
This graduate seminar course investigates current research topics in image-based, data-driven graphics and vision. We will examine data sources, features, and algorithms useful for understanding and manipulating visual data. We will pay special attention to methods that harness large-scale or Internet-derived data. Vision topics such as scene understanding and object detection will be linked to graphics applications such as photo editing and image-based rendering. These topics will be pursued through independent reading, class discussion and presentations, and state-of-the-art projects.

The goal of this course is to give students the background and skills necessary to perform research in image-based graphics and vision. Students should understand the strengths and weaknesses of current approaches to research problems and identify interesting open questions and future research directions. Students will hopefully improve their critical reading and communication skills, as well.

Course Requirements

Reading and Summaries

Students will be expected to read one or two papers for each class. For every assigned paper, students must write a two or three sentence summary and identify at least one question or topic of interest for class discussion. Interesting topics for discussion could relate to strengths and weaknesses of the paper, possible future directions, connections to other research, uncertainty about the conclusions of the experiments, etc. Reading summaries must be posted to the class blog by 11:59pm the day before each class. Feel free to reply to other comments on the blog and help each other understanding confusing aspects of the papers. The blog discussion will be the starting point for the class discussion. If you are presenting you don't need to post a summary to the blog.

Class participation

All students are expected to take part in class discussions. If you do not fully understand a paper that is OK. We can work through the unclear aspects of a paper together in class. If you are unable to attend a specific class please let me know ahead of time (and have a good excuse!).

Presentation(s)

Depending on enrollment, students will lead the discussion of one or two papers during the semester. Ideally, students would implement some aspect of the presented material and perform experiments that help understand the algorithms. Presentations and all supplemental material should be ready one week before the presentation date so that students can meet with the instructor, go over the presentation, and possibly iterate before the in-class discussion. For the presentations it is fine to use slides and code from outside sources (for example, the paper authors) but be sure to give credit.

Semester projects

Students are expected to complete a state-of-the-art research project on topics relevant to the course. Students will propose a research topic part way through the semester. After a project topic is finalized, students will meet occasionally with the instructor to discuss progress. Students will present their progress on their semester project twice during the course and the course will end with final project presentations. Students will also produce a conference-formatted write-up of their project. Projects will be published on the this web page. The ideal project is something with a clear enough direction to be completed in a couple of months, and enough novelty such that it could be published in a peer-reviewed venue with some refinement and extension.

Prerequisites

Strong mathematical skills (linear algebra, calculus, probability and statistics) and previous imaging (graphics, vision, or computational photography) courses are needed. It is strongly recommended that students have taken one of the following courses (or equivalent courses at other institutions):

If you aren't sure whether you have the background needed for the course, you can try reading some of the papers below or you can simply come to class during the shopping period.

Textbook

We will not rely on a textbook, although the free, online textbook "Computer Vision: Algorithms and Applications" by Richard Szeliski is a helpful resource.

Grading

Your final grade will be made up from

20% Reading summaries posted to class blog
20% Classroom participation and attendance
20% Research presentation(s)
40% Semester project

Office Hours:

James Hays, Monday and Wednesday 2:00-3:00pm, CIT 375

Schedule

Date	Paper	Paper, Project page, and Material	Presenter
W, Jan 23	Introduction		James
F, Jan 25	The state of vision and graphics		James
Image representations, mid-level vision, and their applications
M, Jan 28	80 million tiny images: a large dataset for non-parametric object and scene recognition. A. Torralba, R. Fergus, W. T. Freeman. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30(11), 2008.	pdf, project page	James
W, Jan 30	Object recognition from local scale-invariant features, David Lowe, ICCV 1999.	pdf, project page	James
F, Feb 1	Video Google: A Text Retrieval Approach to Object Matching in Videos. Sivic, J. and Zisserman, A. Proceedings of the International Conference on Computer Vision (2003)	pdf, project page	James
optional reading	Robust wide baseline stereo from maximally stable extremal regions. J. Matas, O. Chum, U. Martin, and T Pajdla. Proceedings of the British Machine Vision Conference, 2002.	.pdf
M, Feb 4	Histograms of Oriented Gradients for Human Detection. Navneet Dalal and Bill Triggs. In Proceedings of IEEE Conference Computer Vision and Pattern Recognition, 2005.	.pdf	James
W, Feb 6	Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. S. Lazebnik, C. Schmid, and J. Ponce, CVPR 2006.	pdf, project page	James
F, Feb 8	No Classes - Brown closed due to blizzard
M, Feb 11	Scene Completion Using Millions of Photographs. James Hays, Alexei A. Efros. ACM Transactions on Graphics (SIGGRAPH 2007). August 2007, vol. 26, No. 3.	project page	James
W, Feb 13	Matching Local Self-Similarities across Images and Videos. Eli Shechtman and Michal Irani. IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR'07)	project page	James
F, Feb 15	SUN Database: Large-scale Scene Recognition from Abbey to Zoo. J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. IEEE Conference on Computer Vision and Pattern Recognition (CVPR2010).	project page	James
M, Feb 18	No Classes
W, Feb 20	What makes Paris look like Paris? Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. Siggraph 2012.	project page	Hari
Crowd-sourcing and Human Computation
F, Feb 22	Learning to predict where humans look. T. Judd, K. Ehinger, F. Durand, and A. Torralba. IEEE International Conference on Computer Vision (ICCV), 2009.	project page	Hua
M, Feb 25	ImageNet: A Large-Scale Hierarchical Image Database. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei. IEEE Computer Vision and Pattern Recognition (CVPR), 2009	pdf,project page	Zach
W, Feb 27	How do humans sketch objects? Mathias Eitz, James Hays, and Marc Alexa. Siggraph 2012.	project page	Jeroen
F, Mar 1	Recognizing Jumbled Images: The Role of Local and Global Information in Image Classification. Devi Parikh. ICCV 2011	pdf	Ryan
M, Mar 4	Micro Perceptual Human Computation for Visual Tasks. Yotam Gingold, Ariel Shamir, Daniel Cohen-Or. ACM Transactions on Graphics (ToG) 2012	project page	Xiaofeng
W, Mar 6	Project Status Updates.		Everyone
F, Mar 8	Project Status Updates.		Everyone
Texture, Image Manipulation, and Image Synthesis
M, Mar 11	Texture Optimization for Example-based Synthesis. Vivek Kwatra, Irfan Essa, Aaron Bobick and Nipun Kwatra. Siggraph 2005	project page	Vibhu
W, Mar 13	PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing. Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman. Siggraph 2009.	project page	Greg
F, Mar 15	Image Melding: combining inconsistent images using patch-based synthesis. Soheil Darabi, Eli Shechtman, Connelly Barnes, Dan B Goldman, Pradeep Sen. Siggraph 2012.	project page	Jeroen
M, Mar 18	Internal Statistics of a Single Natural Image. Maria Zontak and Michal Irani. CVPR 2011.	pdf, project page	Hua
W, Mar 20	Super-resolution from Internet-scale Scene Matching. Libin "Geoffrey" Sun and James Hays. IEEE International Conference on Computational Photography (ICCP 2012).	project page	Krishna
F, Mar 22	Video Deblurring of Hand-held Cameras using Patch-based Synthesis. Sunghyun Cho, Jue Wang, and Seungyong Lee. Siggraph 2012.	project page	Hobart
M, Mar 25	No Classes
W, Mar 27	No Classes
F, Mar 29	No Classes
M, Apr 1	Eulerian Video Magnification for Revealing Subtle Changes in the World. Hao-Yu Wu, Michael Rubinstein, Eugene Shih, John Guttag, Fredo Durand, William T. Freeman. Siggraph 2012.	project page	Vibhu
W, Apr 3	CG2REAL. M.K. Johnson, K. Dale, S. Avidan, H. Pfister, W.T. Freeman and W. Matusik. IEEE Trans. on Visualization and Computer Graphics 2010.	project page	Chao
F, Apr 5	LabelMe: a Database and Web-based Tool for Image Annotation. B. C. Russell, A. Torralba, K. P. Murphy, W. T. Freeman. International Journal of Computer Vision, 2008.	pdf, project page	Xiaofeng
M, Apr 8	Photo Clip Art. Jean-François Lalonde, Derek Hoeim, Alexei A. Efros, Carsten Rother, John Winn and Antonio Criminisi. ACM Transactions on Graphics (SIGGRAPH 2007).	project page	Valay
W, Apr 10	Project Status Updates.		Everyone
F, Apr 12	Project Status Updates.		Everyone
M, Apr 15	Sketch2Photo: Internet Image Montage. ACM SIGGRAPH ASIA 2009, ACM Transactions on Graphics. Tao Chen, Ming-Ming Cheng, Ping Tan, Ariel Shamir, Shi-Min Hu.	project page	Yipin
W, Apr 17	Transfusive Image Manipulation. Kaan Yücer, Alec Jacobson, Alexander Hornung, Olga Sorkine. Siggraph Asia 2012	project page	Hobart
Attribute-based Representations
F, Apr 19	Describing Objects by Their Attributes. A. Farhadi, I. Endres, D. Hoiem, and D.A. Forsyth. CVPR 2009	project page	Zach
M, Apr 22	SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes. Genevieve Patterson and James Hays. CVPR 2012	project page	Hari
W, apr 24	Relative Attributes. Devi Parikh and Kristen Grauman. ICCV 2011.	project page	Krishna
Intrinsic Images: factorizing illumination and reflectance
F, Apr 26	Ground-truth dataset and baseline evaluations for intrinsic image algorithms. R. Grosse, M.K. Johnson, E.H. Adelson and W.T. Freeman. ICCV 2009	project page	Valay
M, Apr 29	User Assisted Intrinsic Images. Adrien Bousseau, Sylvain Paris, Fredo Durand. Siggraph Asia 2009	project page	Ryan
W, May 1	Estimating Intrinsic Images from Image Sequences with Biased Illumination. Yasuyuki Matsushita, Stephen Lin, Sing Bing Kang, and Heung-Yeung Shum. 2004	project page	Greg
F, May 3	Coherent Intrinsic Images from Photo Collections. Pierre-Yves Laffont, Adrien Bousseau, Sylvain Paris, Fredo Durand, and George Drettakis. Siggraph Asia 2012.	project page	Yipin
M, May 6	Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views. Pierre-Yves Laffont, Adrien Bousseau, George Drettakis. TVCG 2013.	project page	Chao
Thursday, May 16 2pm (final exam slot)	Final Project Presentations		Everyone

Acknowledgements

Ideas for the organization and content of this course came from many other researchers such as Svetlana Lazebnik, Kristin Grauman, Antonio Torralba, Derek Hoeim, and Alexei Efros.

Related Graduate Seminars at other Universities (although these have more of a vision and learning emphasis)

Learning-Based Methods in Vision (Alexei Efros, CMU and Leonid Sigal, Disney Research Pittsburgh)
Object Recognition (Kristin Grauman, UT )
Object Recognition and Scene Understanding (Antonio Torralba, MIT)
Machine Learning Techniques in Image Analysis (Svetlana Lazebnik, UNC)
Internet Vision (Tamara Berg, Stony Brook)
Visual Scene Understanding (Derek Hoiem, UIUC)