Paper presentations

Each lecture, we will review two papers from the reading list. One student will be responsible to present each paper and to guide the corresponding in-class discussion. When it is your turn to present, I will ask you to email me your slides (in powerpoint, keynote, or PDF format) by 11:59pm of the day before the lecture. You should aim for a 40 minute presentation. A rule of thumb is to devote about 20 minutes to describe the objectives of the work and the proposed technical solution. About 5-10 minutes should be dedicated to presenting the experimental results. Finally, you should prepare a 10 minute discussion highlighting the contributions of the work (what differentiates this paper from previous work?), its strengths as well as its weaknesses (technical, applicative, or experimental). Don't be afraid to be controversial or to ask questions/opinions to your classmates: it is your responsibility to lead an interactive discussion session and you are free to choose the style. Try to conclude your presentation with a list of suggested extensions of the work presented. Don't simply report the future work items discussed in the conclusion section of the paper: think independently about how you would choose to continue the research addressed in the article.

Written critiques

All students must email me a short written critique (between half a page and one page of text) of each paper presented in class by 11:59pm of the day before the lecture. Please summarize in a couple of sentences the objectives and the technical approach. Discuss in more detail the contributions, strengths and weaknesses of the work, exactly as if you were to present the paper in class. Don't forget to include your list of proposed extensions to the method. I will review all critiques biweekly and provide detailed feedback.

We will be reviewing the following papers (starred papers will not be presented but you are encouraged to read them as additional material if you are interested):

Content-based image search in large databases.

• Video Google: A Text Retrieval Approach to Object Matching in Videos.
  Josef Sivic, Andrew Zisserman.
  International Conference on Computer Vision (ICCV), 2003.
  
  
• Scalable recognition with a vocabulary tree.
  David Nister, Henrik Stewenius.
  Conference on Computer Vision and Pattern recognition (CVPR), 2006.
  
  
• Small Codes and Large Image Databases for Recognition.
  Antonio Torralba, Rob Fergus, Yair Weiss.
  Conference on Computer Vision and Pattern recognition (CVPR), 2008.
  
  
• Learning Query-dependent Prefilters for Scalable Image Retrieval.
  Lorenzo Torresani, Martin Szummer, Andrew Fitzgibbon.
  Conference on Computer Vision and Pattern recognition (CVPR), 2009.
  
  
• *80 million tiny images: a large dataset for non-parametric object and scene recognition.
  Antonio Torralba, Rob Fergus, and Bill Freeman.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2008.
  
  
• *Fast Image Search for Learned Metrics.
  B. Kulis, P. Jain, and K. Grauman.
  Conference on Computer Vision and Pattern recognition (CVPR), 2008.
  
  

Annotation and geolocation of personal photos via image search.

• AnnoSearch: Image Auto-Annotation by Search.
  Xinjing Wang, Lei Zhang, Feng Jing, Wei-Ying Ma.
  Conference on Computer Vision and Pattern recognition (CVPR), 2006.
  
  
• IM2GPS : estimating geographic information from a single image.
  James Hays, Alexei A. Efros.
  Conference on Computer Vision and Pattern recognition (CVPR), 2008.
  
  
• Image Sequence Geolocation with Human Travel Priors.
  Evangelos Kalogerakis, Olga Vesselova, James Hays, Alexei A. Efros, Aaron Hertzmann.
  International Conference on Computer Vision (ICCV), 2009.
  
  
• *Geo-located image analysis using latent representations.
  Marco Cristani, Alessandro Perina, Umberto Castellani, Vittorio Murino.
  Conference on Computer Vision and Pattern recognition (CVPR), 2008.
  
  

Learning visual recognition models from text-based image search.

• A Visual Category Filter for Google Images.
  Rob Fergus, Pietro Perona, Andrew Zisserman.
  European Conference on Computer Vision (ECCV), 2004.
  
  
• Learning Object Categories from Google's Image Search.
  Rob Fergus, Li Fei-Fei, Pietro Perona, Andrew Zisserman.
  International Conference on Computer Vision (ICCV), 2005.
  
  
• Keywords to Visual Categories: Multiple-Instance Learning for Weakly Supervised Object Categorization.
  Sudheendra Vijayanarasimhan, Kristen Grauman.
  Conference on Computer Vision and Pattern recognition (CVPR), 2008.
  
  
• OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning..
  Li-Jia Li, Gang Wang, Li Fei-Fei.
  Conference on Computer Vision and Pattern recognition (CVPR), 2007.
  
  

Photo editing using large image databases.

• Scene Completion Using Millions of Photographs.
  James Hays, Alexei A. Efros.
  ACM Transactions on Graphics (SIGGRAPH), 2007.
  
  
• Photo Clip Art.
  Jean-Francois Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John Winn, Antonio Criminisi.
  ACM Transactions on Graphics (SIGGRAPH), 2007.
  
  
• Creating and Exploring a Large Photorealistic Virtual Space.
  Josef Sivic, Biliana Kaneva, Antonio Torralba, Shai Avidan, William T. Freeman.
  First IEEE Workshop on Internet Vision, 2008.
  
  
• SIFT flow: dense correspondence across difference scenes.
  Ce Liu, Jenny Yuen, Antonio Torralba, Josef Sivic, William T. Freeman.
  European Conference on Computer Vision (ECCV), 2004.
  
  

Learning aesthetic properties of photographs.

• The Design of High-Level Features for Photo Quality Assessment.
  Yan Ke, Xiaoou Tang, Feng Jing.
  Computer Vision and Pattern Recognition (CVPR), 2006.
  
  
• Using Color Compatibility for Assessing Image Realism.
  Jean-Francois Lalonde, Alexei A. Efros.
  International Conference on Computer Vision (ICCV), 2007.
  
  
• *Studying Aesthetics in Photographic Images Using a Computational Approach.
  Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang.
  European Conference on Computer Vision (ECCV), 2006.
  
  

Unsupervised foreground clustering.

• Foreground Focus: Unsupervised Learning from Partially Matching Images.
  Yong Jae Lee, Kristen Grauman.
  International Journal of Computer Vision (IJCV), 2008.
  
  
• Using Multiple Segmentations to Discover Objects and their Extent in Image Collections.
  Bryan C. Russell, Alexei A. Efros, Josef Sivic, William T. Freeman, Andrew Zisserman.
  Computer Vision and Pattern Recognition (CVPR) 2006.
  
  

Learning joint models of text and visual appearance.

• Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary.
  Pinar Duygulu, Kobus Barnard, Nando de Freitas, David Forsyth.
  European Conference on Computer Vision (ECCV), 2002.
  
  
• Names and Faces in the News.
  Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsyth.
  Computer Vision and Pattern Recognition (CVPR) 2004.
  
  
• *Matching words and pictures.
  Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan.
  Journal of Machine Learning Research (JMLR), 2003.
  
  

Photo tourism: 3D navigation of photo collections.

• Photo tourism: Exploring photo collections in 3D.
  Noah Snavely, Steven M. Seitz, Richard Szeliski.
  ACM Transactions on Graphics (SIGGRAPH), 2006.
  
  
• Building Rome in a Day.
  Sameer Agarwal, Noah Snavely, Ian Simon, Steven M. Seitz and Richard Szeliski.
  International Conference on Computer Vision (ICCV), 2009.
  
  

Computing iconic summaries.

• Computing Iconic Summaries of General Visual Concepts.
  Rahul Raguram, Svetlana Lazebnik.
  First IEEE Workshop on Internet Vision, 2008.
  
  
• Scene Summarization for Online Image Collections.
  Ian Simon, Noah Snavely, and Steven M. Seitz.
  International Conference on Computer Vision (ICCV), 2007.