The CVPR conference in Boston, one of the premier computer vision conferences, was all about convolutional neural network and deep learning. This new (or not so new) techniques seem to be doing everything from image classification to scene understanding. Although the vision community has not shown too much of an interest in robotic applications, I had a feeling that this seems to change (slowly at least).
tl;dr:CVPR is huge, lots of convolutional neural network, which is now the de-facto standard on how to tackle computer vision problems. CV research is getting more easily to reproduce thanks to open source code AND models. There is a trend to investigate more what is behind these networks and also a trend to look at more robotic (real-world) applications of vision. My longer write-up of #CVPR2015 is after the break. Others have done similar things: a great write-up Tomasz Malisiewicz, another one by Zoya Bylinskiilisting interesting CVPR 2015 papers.
One of the clearly visible impacts of the extensive use of computer vision techniques in industry was the presence of quite a few internet heavy-weights, such as Amazon, Baidu, Google, Facebook, Microsoft, Tesla, … (Google has a blog post listing their involvement in CVPR papers, workshops and tutorials.) Seems there is a demand for computer vision researchers. The converence itself was split into tutorials (1 day), conference (3d) and workshops (2d).
The highly anticipated plenary talk by Yann LeCun, about what is wrong with deep learning, was though interesting a bit of a let-down, especially given the intriguing title. The second plenary was given by Jack Gallent a neuroscientist from UC Berkeley. His talk featured the work in his lab, which focusses on understanding, especially the mid-level parts of, human visual perception by using fMRI scans.
The tutorials followed this trend and the rooms were bursting during the presentations of caffe, a deep learning library, and torch7, a machine learning library originating at IDIAP and now heavily used at Google, Facebook, Twitter and others. Presenters slides can be found on the respective webpages (caffe, torch7).
This is a list of interesting papers and posters that I found. The program listing all papers can be found here.
Papers that sound interesting
Limited time means I have not read all of the following ones, but they sound interesting :) (this might change once I get to read more). I tried to categorize them a little (action recognition is obviously quite interesting for us in robotics).