Posted by Danny TarlowI like this Python class design idea:
Over the last few years energy minimization has emerged as an indispensable tool in computer vision. The reason for this rising popularity has been the successes of efficient Graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object recognition, and stereo reconstruction. This tutorial will explain how these algorithms work, and what classes of minimization problems they can solve.The nice part of the talk is that he went far beyond the standard case, where you have only two classes (e.g., foreground and background) with submodular potentials (crudely: nearby pixels should prefer to take the same label). In the past 5 or so years, some serious advances have been made in making these methods applicable to a wide variety of interesting, realistic problems. He talked about the non-discrete case, the non-submodular case, the multi-label case, and the higher-order potentials case.
Existing object recognition methods are not practical to use on huge image collections found on the Internet and elsewhere. Recently, a number of computer vision approaches have been proposed that scale to millions of images. At their core, many of them rely on machine learning techniques to learn compact representations that can be used in efficient indexing schemes. My talk will review this work, highlighting the common learning techniques used and discuss open problems in this area.The nice part about this talk was that he chose approaches that were extremely practical. Rather than having a method (say, machine learning) that you want to apply to a problem whether it fits or not, this was a more gentle approach, using efficient algorithms and data structures, then only applying a small amount of machine learning in the cases where it produced the largest gains -- in this case, learning hash functions.
READING GROUP: A critical review of old ideas, with treasure hunt
Computer Vision is a fairly "horizontal" field, where often ideas are proposed, forgotten, and then re-invented. This is typical of nascent fields and "high entropy" research environments. At various points, it is useful to revisit old literature, and try to read them in a modern key. What ideas survived? Have they taken "new forms"? What leads have turned into dead-ends? In this reading group we will attempt such a re- reading for two authors that influenced the field in its beginning, but whose influence has waned over the years.
Students will be asked to read two books: One is David Marr's 1980 book Vision. The other is James Jerome Gibson's 1986 book "The ecological approach to visual perception". More than 20 years have passed, which is enough time to reflect.
Students will be asked to compile a list of "ideas" or "leads" proposed or discussed by these authors. For each idea, they will be asked to evaluate it in relation to the current literature: Is this idea still valid? Is it reflected in the current literature? Has this idea been proven fruitless? Has it been forgotten but may still be worthy of pursuit? They will be asked to give a few examples from the modern literature (say ICCV/CVPR/ECCV proceedings from the past 4-5 years) where this idea is adopted or disputed.
The competition will then proceed as follows: Students will be called to present and describe these ideas, and discuss whether it is a "thumbs up" or a "thumbs down" idea. If the student manages to convince the audience, the idea is put into a bucket. If others have found the same idea, the number of people that found it is a "score" for that idea.
There will be two prizes for the competition. One goes to the individual or group that found the most number of ideas. The prize will be a bottle of Champaign to share among the winners. The second prize ($1000) will be for the treasure hunt, and will be given to the individual that will have found one idea that he/she can convince the audience is worth pursuing, and that nobody else in the audience has found.
Students will be asked to submit their list of ideas found on the first day of the school. This competition will require that students consult the library of their institution ahead of time, and plan enough time to read these books, possibly more than once, in relation to current literature.
This Number Crunching Life