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.

Google Internship in Vision/ML

Disclaimer: the following post is cross-posted from Yaroslav's "Machine Learning, etc" blog. Since I always rave about my experiences at Google as an intern (did it twice!), I thought some of my fellow readers would find this information useful.  If you are a vision PhD student at CMU or MIT, feel free to ask me more about life at Google.  If you have questions regarding the following internship offer, you'll have to ask Yaroslav.

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Original post at: http://yaroslavvb.blogspot.com/2011/10/google-internship-in-visionml.html

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My group has intern openings for winter and summer. Winter may be too late (but if you really want winter, ping me and I'll find out feasibility). We use OCR for Google Books, frames from YouTube videos, spam images, unreadable PDFs encountered by the crawler, images from Google's StreetView cameras, Android and few other areas. Recognizing individual character candidates is a key step in OCR system. One that machines are not very good at. Even with 0 context, humans are better. This shall not stand!

For example, when I showed the picture below to my Taiwanese coworker he immediately said that these were multiple instance of Chinese "one".



Here are 4 of those images close-up. Classical OCR approaches, have trouble with these characters.



This is a common problem for high-noise domain like camera pictures and digital text rasterized at low resolution. Some results suggest that techniques from Machine Vision can help.

For low-noise domains like Google Books and broken PDF indexing, shortcomings of traditional OCR systems are due to
1) Large number of classes (100k letters in Unicode 6.0)
2) Non-trivial variation within classes
Example of "non-trivial variation"


I found over 100k distinct instances of digital letter 'A' from just one day's crawl worth of documents from the web. Some more examples are here

Chances are that the ideas for human-level classifier are out there. They just haven't been implemented and tested in realistic conditions. We need someone with ML/Vision background to come to Google and implement a great character classifier.

You'd have a large impact if your ideas become part of Tesseract. Through books alone, your code will be run on books from 42 libraries. And since Tesseract is open-source, you'd be contributing to the main OCR effort in the open-source community.

You will get a ton of data, resources and smart people around you. It's a very low bureocracy place. You could run Matlab code on 10k cores if you really wanted, and I know someone who has launched 200k core jobs for a personal project. The infrastructure also makes things easier. Google's MapReduce can sort a petabyte of data (10 trillion strings) with 8000 machines in just 30 mins. Some of the work in our team used features coming from distributed deep belief infrastructure.


In order to get an internship position, you must pass general technical screen that I have no control of. If you are interested in more details, you could contact me directly.  -- Yaroslav

(the link to apply is usually here, but now it's down, will update when it's fixed)