Thursday, July 18, 2013

[CVPR 2013] Three Trending Computer Vision Research Areas

As I walked through the large poster-filled hall at CVPR 2013, I asked myself, “Quo vadis Computer Vision?" (Where are you going, computer vision?)  I see lots of papers which exploit last year’s ideas, copious amounts of incremental research, and an overabundance of off-the-shelf computational techniques being recombined in seemingly novel ways.  When you are active in computer vision research for several years, it is not rare to find oneself becoming bored by a significant fraction of papers at research conferences.  Right after the main CVPR conference, I felt mentally drained and needed to get a breath of fresh air, so I spent several days checking out the sights in Oregon.  Here is one picture -- proof that the CVPR2013 had more to offer than ideas!

When I returned from sight-seeing, I took a more circumspect look at the field of computer vision.  I immediately noticed that vision research is actually advancing and growing in a healthy way.  (Unfortunately, most junior students have a hard determining which research papers are actually novel and/or significant.)  A handful of new research themes arise each year, and today I’d like to briefly discuss three new computer vision research themes which are likely to rise in popularity in the foreseeable future (2-5 years).

1) RGB-D input data is trending.

Many of this year’s papers take a single 2.5D RGB-D image as input and try to parse the image into its constituent objects.  The number of papers doing this with RGBD data is seemingly infinite.  Some other CVPR 2013 approaches don’t try to parse the image, but instead do something else like: fit cuboids, reason about affordances in 3D, or reason about illumination.  The reason why such inputs are becoming more popular is simple: RGB-D images can be obtained via cheap and readily available sensors such as Microsoft’s Kinect.  Depth measurements used to be obtained by expensive time of flight sensors (in the late 90s and early 00s), but as of 2013, $150 can buy you one these depth sensing bad-boys!  In fact, I had bought a Kinect just because I thought that it might come in handy one day -- and since I’ve joined MIT, I’ve been delving into the RGB-D reconstruction domain on my own.  It is just a matter of time until the newest iPhone has an on-board depth sensor, so the current line of research which relies on RGB-D input is likely to become the norm within a few years.

H. Jiang and J. Xiao. A Linear Approach to Matching Cuboids in RGBD Images. In CVPR 2013. [pdf] [code]

2) Mid-level patch discovery is a hot research topic.
Saurabh Singh from CMU introduced this idea in his seminal ECCV 2012 paper, and Carl Doersch applied this idea to large-scale Google Street-View imagery in the “What makes Paris look like Paris?” SIGGRAPH 2012 paper.  The idea is to automatically extract mid-level patches (which could be objects, object parts, or just chunks of stuff) from images with the constraint that those are the most informative patches.  Regarding the SIGGRAPH paper, see the video below.

Unsupervised Discovery of Mid-Level Discriminative PatchesSaurabh Singh, Abhinav Gupta, Alexei A. Efros. In ECCV, 2012.

Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. What Makes Paris Look like Paris? In SIGGRAPH 2012. [pdf]
At CVPR 2013, it was evident that the idea of "learning mid-level parts for scenes" is being pursued by other top-tier computer vision research groups.  Here are some CVPR 2013 papers which capitalize on this idea:
Blocks that Shout: Distinctive Parts for Scene Classification. Mayank Juneja, Andrea Vedaldi, CV Jawahar, Andrew Zisserman. In CVPR, 2013. [pdf]
Representing Videos using Mid-level Discriminative Patches. Arpit Jain, Abhinav Gupta, Mikel Rodriguez, Larry Davis. CVPR, 2013. [pdf]
Part Discovery from Partial Correspondence. Subhransu Maji, Gregory Shakhnarovich. In CVPR, 2013. [pdf]
3) Deep-learning and feature learning are on the rise within the Computer Vision community.
It seems that everybody at Google Research is working on Deep-learning.  Will it solve all vision problems?  Is it the one computational ring to rule them all?  Personally, I doubt it, but the rising presence of deep learning is forcing every researcher to brush up on their l33t backprop skillz.  In other words, if you don't know who Geoff Hinton is, then you are in trouble.

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