cvpr 2011: highlights from day 1

Today was the first main day of the CVPR 2011 conference.  Here are some papers which I found particularly exciting and a brief reason why they are super cool:

Becuase you can’t afford to miss this one:

Abhinav Gupta, Scott Satkin, Alexei A. Efros and Martial Hebert. From Scene Geometry to Human Workspace. In CVPR 2011.

Because large scale structure from motion meets graphical models:

David Crandall, Andrew Owens, Noah Snavely, and Dan Huttenlocher. Discrete-Continuous Optimization for Large-Scale Structure from Motion. In CVPR 2011.

Because segmentation provides a good basis for object discovery:

Galleguillos C., McFee B., Belongie S., Lanckriet G. From Region Similarity to Category Discovery. In CVPR 2011.

Because imitation provides a category-free way of understanding pose:

Graham Taylor, Ian Spiro, Christoph Bregler, and Rob Fergus. Learning Invarance through Imitation. In CVPR 2011. Project page (with links to supplementary material)

Because you don’t need to solve intractable graphical model inferences:

S. Ross, D. Munoz, M. Hebert, J. A. Bagnell. Learning Message-Passing Inference Machines for Structured Prediction.  In CVPR 2011.

Because a little bit of bottom-up never hurt a lot of top-down:

Thomas Brox, Lubomir Bourdev, Subhransu Maji, Jitendra Malik. Object Segmentation by Alignment of Poselet Activations to Image Contours. In CVPR 2011

Because you might want to perform segmentation simultaneously on several related images:

Sara Vicente, Carsten Rother and Vladimir Kolmogorov. Object Cosegmentation. In CVPR 2011.

I missed some of the posters later in the evening because I went for a short hike organized by Jianbo Shi (to Seven Falls).  After all, CVPR is in Colorado Springs, and it is silly to stay inside the conference the entire time.   Below is a pic of the falls (only a short drive from the CVPR11 conference hotel). 

After several hours of talks it was good to get some fresh air and see some waterfalls in Colorado Springs!  On Friday, I'm going on the Pikes Beak downhill bike ride with some friends and it should be super-fun.

Learning and Inference in Vision: from Features to Scene Understanding

Tomorrow, Jonathan Huang and I are giving a Computer Vision tutorial at the First MLD (Machine Learning Department) Research Symposium at CMU. The title of our presentation is Learning and Inference in Vision: from Features to Scene Understanding.

The goal of the tutorial is to expose Machine Learning students to state-of-the-art object recognition, scene understanding and the inference problems associated with such high-level recognition problems. Our target audience is graduate students with little or no prior exposure to object recognition who would like to learn more about the use of probabilistic graphical models in Computer Vision. We outline the difficulties present in object recognition/detection and outline several different models for jointly reasoning about multiple object hypotheses.