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Tampilkan postingan dengan label jay yagnik. Tampilkan semua postingan

Rabu, 26 Juni 2013

[Awesome@CVPR2013] Scene-SIRFs, Sketch Tokens, Detecting 100,000 object classes, and more

I promised to blog about some more exciting papers at CVPR 2013, so here is a short list of a few papers which stood out.  This list also include this year's award winning paper: Fast, Accurate Detection of 100,000 Object Classes on a Single Machine.  Congrats Google Research on the excellent paper!



This paper uses ideas from Abhinav Gupta's work on 3D scene understanding as well as Ali Farhadi's work on visual phrases; however, it also uses RGB-D input data (like many other CVPR 2013 papers).

W. Choi, Y. -W. Chao, C. Pantofaru, S. Savarese. "Understanding Indoor Scenes Using 3D Geometric Phrases" in CVPR, 2013. [pdf]

This paper shows a uses the crowd to learn which parts of birds are useful for fine-grained categorization.  If you work on fine-grained categorization or run experiments with MTurk, then you gotta check this out!
Fine-Grained Crowdsourcing for Fine-Grained Recognition. Jia Deng, Jonathan Krause, Li Fei-Fei. CVPR, 2013. [ pdf ]

This paper won the best paper award.  Congrats Google Research!

Fast, Accurate Detection of 100,000 Object Classes on a Single Machine. Thomas Dean, Mark Ruzon, Mark Segal, Jon Shlens, Sudheendra Vijayanarasimhan, Jay Yagnik. CVPR, 2013 [pdf]


The following is the Scene-SIRFs paper, which I thought was one of the best papers at this year's CVPR.  The ideas to to decompose an input image into intrinsic images using Barron's algorithm which was initially shown to work on objects, but now is being applied to realistic scenes.

Intrinsic Scene Properties from a Single RGB-D Image. Jonathan T. Barron, Jitendra Malik. CVPR, 2013 [pdf]


This is a graph-based localization paper which uses a sort of "Visual Memex" to solve the problem.
Graph-Based Discriminative Learning for Location Recognition. Song Cao, Noah Snavely. CVPR, 2013. [pdf]


This paper provides an exciting new way of localizing contours in images which is orders of magnitude faster than the gPb.  There is code available, so the impact is likely to be high.

Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection. Joseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar. CVPR 2013. [ pdf ] [code@github]

Rabu, 24 Agustus 2011

The vision hacker culture at Google ...

I sometimes get frustrated when developing machine learning algorithms in C++.  And since working in object recognition basically means you have to be a machine learning expert, trying something new and exciting in C++ can be extremely painful.  I don't miss the C++ heavy workflow for vision projects at Google.  C++ is great for building large-scale systems, but not for pioneering object recognition representations.  I like to play with pixels and I like to think of everything as matrices.  But programming languages, software engineering philosophies, and other coding issues aren't going to be today's topic.  Today I want to talk about the one thing that is more valuable that is computers, and that is people.  Not just people, but a community of people, and in particular the culture at Google -- in particular, vision@Google.


I miss being around the hacker culture at Google.  

The people at Google aren't just hackers, they are Jedis when it comes to building great stuff -- and that is why I recommend a Google internship to many of my fellow CMU vision Robograds (fyi, Robograds are CMU Robotics Graduate Students).  CMU-ers, like Googlers, like to build stuff.  However, CMU-ers are typically younger.


What is a software engineering Jedi, you might ask? Tis' one who is not afraid of million cores, one who is not afraid of building something great.  While little boys get hurt by the guns 'n knives of C++, Jedi use their tools like ninjas use their swords. You go into Google as a boy, you come out a man.  NOTE: I do not recommend going to Google and just toying around in Matlab for 3 months.  Build something great, find a Yoda-esque mentor, or at least strive to be a Jedi.  There's plenty of time in grad school for Matlab and writing papers.  If you get a chance to go to Google, take the opportunity to go large-scale and learn to MapReduce like the pros.

Every day I learn about more and more people I respect in vision and learning going to Google, or at least interning there (e.g., Andrej Karpathy who is starting his PhD@Stanford and Santosh Divvala who is a well-known CMU PhD student and vision hacker).  And I really can't blame them for choosing Google over places like Microsoft for the summer.  I can't think of many better places to be -- the culture is inimitable.  I spent two summers at Jay Yagnik's group some of the great people I interned with are already full-time Googlers (e.g. Luca Bertelli and Mehmet Emre Sargin).  And what is really great about vision@google is that these guys get to publish surprisingly often!  Not just throw-away-code kind of publish, but stuff that fits inside large-scale systems -- stuff which is already inside Google products.  The technology is often inside the Google product before the paper goes public!  Of course it's not easy to publish at a place like Google because there is just way too much exciting large-scale stuff going on.  Here is a short list of some cool 2010/2011 vision papers (from vision conferences) with significant Googler contributions.



Kernelized Structural SVM Learning

“Kernelized Structural SVM Learning for Supervised Object Segmentation”, Luca BertelliTianli Yu, Diem Vu, Burak Gokturk, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2011.
[abstract] [pdf]


Finding Meaning on YouTube


“Finding Meaning on YouTube: Tag Recommendation and Category Discovery”, George Toderici,Hrishikesh AradhyeMarius Pasca, Luciano Sbaiz, Jay YagnikComputer Vision and Pattern Recognition, 2010.
[abstract] [pdf]




Here is a very exciting and new paper from SIGGRAPH 2011.  It is a sort of Visual Memex for faces -- congratulations on this paper, guys!  Check out the video below.

Exploring Photobios Movie







Exploring Photobios from Ira Kemelmacher on Vimeo




Ira Kemelmacher-Shlizerman, Eli Shechtman, Rahul Garg, Steven M. Seitz. "Exploring Photobios." ACM Transactions on Graphics (SIGGRAPH), Aug 2011. [pdf]



Finally, here is a very mathematical paper with a sexy title from the vision@google team.  It will be presented at the upcoming ICCV 2011 Conference in Barcelona -- the same conference where I'll be presenting my Exemplar-SVM paper.  


The Power of Comparative Reasoning
Jay Yagnik, Dennis Strelow, David Ross, Ruei-Sung Lin. ICCV 2011. [PDF]


P.S. If you're a fellow vision blogger, then come find me in Barcelona@iccv2011 -- we'll go brag a beer.