Google Scholar, My Citations, a new paradigm for finding great Computer Vision research papers

I have been finding great computer vision research papers by using Google Scholar for the past 2+ years.  My recipe is straightforward and has two key ingredients. First, by finding new papers that cite one of my published papers, I automatically get to read papers which will be relevant to my own research interests.  The best bit is that by using Google Scholar, I'm not limiting my search to a single conference -- Google finds papers from the raw web.

Second, I have a short list of superstar vision researchers (Jitendra Malik, among others) and I basically read anything and everything these gurus publish.  Regularly visiting academic homepages is the best way to do this, but Google Scholar also lets me search by name.  In addition, nobody lists on their homepage their papers' citation counts.  This means if I visit a researcher's personal website, I have to make a decision as to what paper to read based on (title, co-authors, publication venue).  But highly-cited papers are likely to be more important to read first.  I believe that this is a good rule of thumb, and very important if you are new to the field.

I am really glad that Google finally let researchers make public profiles to view their papers and see their citations, etc.  See Google Scholar blog for more information.  I've been using statcounter to monitor my blog's visitors, and now I can use Google Scholar to monitor who is citing my research papers!  I'm not claiming that the only way for me to read one of  your papers is to cite one of my papers, but believe me, even if we never met at a vision conference, if you cited one one my papers there's a good chance I already know about your research :-)  I would love to see Google Scholar Citations pages one day replace "my publications" sections on academic homepages...

My Citations screenshot

My only complaint with Google Scholar is that I can't seem to get it to recognize my two most recent papers.  I have these papers listed on my homepage, so do my co-authors, but Google isn't picking them up!!!  I manually added them to my Google My Citations page, and using Google Scholar I was able to find at least one other paper which cites on of these two papers.

I read the inclusion guidelines, and I'm still baffled.  The PDFs are definitely over 5MB, but my older papers which were indexed by Google were also over 5MB.  Dear Google, are you seriously not indexing my recent papers because they are over 5MB?  It takes us, researchers, months of hard work to get our work out the door.  We see the sun rise for weeks straight when we are in deadline-mode, and the conferences/journals give us size limitations -- we work hard to make our stuff fit within these limits (something like 20MB per PDF).  And we, researchers, are crazy about Google and what it means for organizing the world's information -- naturally we are jumping on the Google Scholar bandwagon. I really hope there's some silly reason why I can't find my own papers using Google Scholar, but if I can't find my own work, that means others can't find my own work, and until I can be confident that Google Scholar is bug-free, I cannot give it my full recommendation.

Problematic papers for Google Scholar:

Abhinav Shrivastava, Tomasz Malisiewicz, Abhinav Gupta, Alexei A. Efros. Data-driven Visual Similarity for Cross-domain Image Matching. In SIGGRAPH ASIA, December 2011.

Tomasz Malisiewicz, Abhinav Gupta, Alexei A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond. In ICCV, November 2011.

If anybody has any suggestions (there's a chance I'm doing something wrong), or an explanation as to why my papers haven't been indexed, I would love to hear from you.

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.

Image from http://www.rabittooth.com/

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.

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Kernelized Structural SVM Learning

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

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Finding Meaning on YouTube

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

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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]

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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.

cvpr 2011 in colorado springs

CVPR (CVPR 2011) will be held this year in Colorado Springs, and I'll hopefully get a chance to blog about the newest and exciting ideas coming out of the Computer Vision research community while I'm there.  CVPR is a conference which I try to attend every year because 1.) the quality of the work there is superb and 2.) it is a perfect opportunity to network with all the amazing vision researchers I've met over the years.

I will also be at the Fine Grained Visual Categorization workshop on June 25th, which has an exciting schedule of talks and posters.  This is the first ever FGVC workshop, and it is being organized by Ryan Farrell (UMD), Steve Branson (UCSD), and Peter Welinder (Caltech).

CVPR, the A+'s of yesteryear, and robots need us

It is November yet again, and I'm proud to announce my last CVPR submission as a graduate student!  It is that time of the year again -- the post-CVPR downtime.  It is time to mentally tuck away the fruits of our labor (NOTE: you might want to create a readme.txt which explains how to use the 20,000 lines of code you wrote in the 7 days preceding the deadline), consider the long-term impact of our work, and perhaps even reconsider our position in life.

I want to build intelligent machines, and I feel vision is the right place to start -- even roboticists such as Rodney Brooks started out in vision. However, I don't feel churning out 'cute' CVPR papers is going to do much.  Perhaps if all one cares about in life is getting tenure at a top ranked university, then proof-of-concept papers might be the path of least resistance.  But remember when you were a teen, and you wanted to build a rocket which lets you travel at relativistic speeds -- allowing you to go back in time?  Or remember when you wanted to build those humanoid robots that would both entertain your kid sister and help out your mother with house chores?

So why did so many intelligent people I know abandon those grandeur dreams and settle for bread crumbs?  Getting your paper submitted to a peer-reviewed conference, so that you can pad your CV with another publication, is incommensurable with the dreams you once had.  The publication of today is the A+ of yesteryear, and it is just way too easy for us, intellectuals, to stay comfortable with those A's, without asking for more.  But robots need us, CVPR papers won't assemble themselves into intelligent machines.

But the deadline is over, and now its time to relax.  If my rant did not make sense to you, then I envy you.  I have to move on to more positive things -- I need to finish reading Pinker's Blank Slate, read some more Wittgenstein (and fully assimilate his criticism of Augustine's theory of language-acquisition), waste two days playing with the Riemann Zeta function (because the Basel problem was only the beginning), play some guitar, etc.

On Science: passion towards solving a problem must come from within

I'm currently in Chicago while traveling to Vancouver (NIPS 2009 conference) where I'll be defending my research during Tuesday's poster session. Instead of delving into the computational challenges that motivate my research, I want to take a step back and criticize what (sometimes? often?) happens during the publishing cycle.

According to me, good research starts with the passion to solve a particular problem or address a specific concern. Quite often, good research will raise more questions than it successfully solves. Unfortunately, when we submit papers to conferences we are judged on the clarity of presentation, level of experimental validation, as well as overall completeness. This means that the publishing cycle quite often promotes writing "cute" papers that have little long term impact in the field and can only be viewed as thorough and complete due to their narrow scope. This is why we should not solely rely on peer review nor cater our scientific lives towards pleasing others. Sometimes being a good scientist means breaking free from the norms that the world around us rigidly follows, sometimes publishing too often skews our research focus, and sometimes falling off the face of the earth for a period of time is necessary to push science in a new direction.

I want to challenge every scientist to follow their dreams and attempt to solve the problems they truly care about and not just attempt to please peer review. Maybe some think that the perversion of science (that is evaluating scientists by the number of publications they have) is okay, but in my book a scientific career which produces a single grand idea is superior to a career saturated with myriad "cute and thorough" papers. I'm not particularly upset with the progress of Computer Vision, but I think more people should ponder the negative consequences of pulling the publish-trigger too often.