A camera that can see around a corner?

A camera that can see around a corner?


From 22nd to 29th October the International Conference on Computer Vision was held in Venice: the 30th edition of the event can be described by some interesting statistics:

  • More than 3100 attendees (an increase of 113% compared to ICCV2015)
  • An increase of almost 30% in the number of paper submissions
  • There were 65 sponsors this year and the number of exhibitors increased by more than 350%.
  • Most authors were from the USA and China and the most common topics were recognition including detection, categorization and indexing
  • The word most used in the title of a paper was “Learning”, followed by “network” and “image”
  • The organizations that submitted the most papers were Tsinghua University, Carnegie Mellon University and Google

Here I list some of the most popular topics presented in ICCV.

  1. Generative Adversarial Networks (GAN) has become very popular with several applications such as image synthesis from text description, video synthesis from a single image, image style transformation, image super-resolution, or image in-painting (filling missing parts of images). At the beginning of ICCV, a whole day tutorial focused on GANs with a motivating introduction into the area given by Ian Goodfellow showing potential applications and suggesting best-practice. Compared to how this topic was covered earlier this year in the CVPR 2017 event, there has been a great increase of interest in GAN and I am curious to see how this topic will develop in future.
  2. Many papers focused on semantic and instance-level segmentation and the best paper award was assigned to one of these, entitled “Mask R-CNN” prepared by He et al. The trend I noticed is that semantic and instance-level segmentation is slowly becoming a more popular topic than the classic problem of object detection.
  3. A hot topic within ICCV 2017 was activity recognition in videos with Visual Question Answering (VQA), as I already mentioned in my blog post about CVPR. VQA naturally links to understanding content from videos, which is a task that is much more time-consuming than understanding the content in a picture. I expect that once VQA for videos starts to emerge, it may lead to huge improvements in video analysis.
  4. The work that most impressed me was ‘Turning Corners into Cameras’ from Bouman et al. With a demonstration performed next to the poster, the authors showed how to detect (and in some cases track) movements of a target that was obscured behind a corner of an object using a standard RGB camera. The source code is available for testing and I think this is astonishing: it sounds like magic – to have a camera that can see what is around a corner!
Pavel Dvorak
Pavel Dvorak
Research Specialist CV Area