从CVPR 2013看计算机视觉的研究领域和趋势 [CVPR 2013] Three Trending Computer Vision Research Areas
[CVPR 2013] Three Trending Computer Vision Research Areas
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.
allowfullscreen="" frameborder="0" height="315" src="http://www.youtube.com/embed/s5-30NKSwo8" width="560"> 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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