Göm meny

Reading Group in Computer and Robot Vision

  • The purpose of this reading group is to provide an overview of the rapidly evolving computer vision literature. We meet once a week to discuss an article that all participants should read before the seminar.
  • All participants are welcome to suggest, and present articles of their choosing. The theme for articles should be computer and robot vision, with emphasis on new, high-impact conference papers (e.g. from ICCV, CVPR, RSS, or ECCV). To make best use of time, you may consider choosing an article that relates to your work, that you presumably would read anyway. If the article is well written that is also a plus.
  • You have the option to attend the seminars as part of a PhD course. You will get one 1hp for each time you present a paper, and participate in another three seminars. If you want to go for the PhD course option, let me know in advance by sending me an email with you personal number, so I can register your attendance.
    / Per-Erik

  • Meeting room: Visionen, Stora Konf.rummet, Campus Valla, Building B.

  • Time: Wednesdays at 13.15-14.00.

  • E-mail list: Upcoming articles are announced on the mailing list vision-seminars.

Upcoming articles

  • The article club has a summer break. We will start again in August.

Article suggestions

  • Have a look at e.g. the following proceedings: CVPR'18, ECCV'18, ICCV'17, NIPS'18, SIGGRAPH'18, RSS'2018, ACCV'17. Some old, unused suggestions are listed below.
  • A. Ilyas et al., "Adversarial Examples Are Not Bugs, They Are Features", ArXiv 2019 [PDF] [blog post] SD190515
  • C. Chen et al. R-CNN for Small Object Detection, MERL technical report [PDF] SD170531

Paper log spring 2019

  • March 6: Mikael presents W. Brendel and M. Bethge, Approximating CNNs with Bag-of-local features models works surprisingly well on ImageNet, ICLR 2019. [PDF] [reviews]
  • March 27: Amanda presents: S. Mukherjee, H. Asani, E. Lin, S. Kannan, "ClusterGAN: Latent Space Clustering in Generative Adversarial Networks. ArXiV 2018 [PDF] [Github]
  • April 17: Joakim presents: A. Graves, G. Wayne, I. Danihelka. Neural Turing Machines, ArXiv 2014 [PDF]. Further reading: Nature 2016 [PDF], NIPS 2016 Talk: [YouTube]
  • May 8: Gustav presents: A. Kar, C. Häne, and J. Malik, "Learning a Multi-View Stereo Machine", NIPS'17 [Paper+reviews]
  • May 15: Oliver presents: J. Tremblay et al., "Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization", CVPR18 [PDF] [talk]

Old paper logs

Senast uppdaterad: 2019-05-27