Hi and welcome. My name is Michael Gygli and I am a Research Scientist at Google Zurich in the group of Prof. Vittorio Ferrari.
Previously I was the Head of AI at gifs.com, where I led the Machine Learning and Computer Vision efforts aimed at making video editing and gif creation intelligent and easy. Before that, I was a PhD student under the supervision of Prof. Luc Van Gool at the Computer Vision Laboratory of ETH Zurich. The research during my PhD was focused on video summarization, but I am also interested in deep learning, affective attributes, submodularity and data summarization in general.
Check the publications page or my Google Scholar profile for previous publications. I also provide code for some of my work on Github.
Slides for my CVPR tutorial talk "Video Summarization as Subset Selection": Click here
That's me (was, a few years ago)
| 2018 December: | Our group at Google is looking for PhD interns. Please reach out if you are interested. |
| 2018 February: | I joined Google as a Research Scientist. |
| 2017 December: | We have released a new dataset for personalized highlight detection with data from more than 13000 users. |
| 2017 July: | The PathTrack paper was accepted to ICCV and the query-adaptive summarization paper to ACM Multimedia. |
| 2017 June: | Video and slides of my presentation "CNNs in Video analysis - An overview, biased to fast methods" at the ML meetup Zurich are now online. |
| 2017 June: | Code for our ICML paper on Deep Value Networks is now available on github. |
| 2017 May: | Our pre-print Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks is now on arXiv. |
| 2017 Jan: | I joined gifs.com |
| arXiv 2018 | PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation |
| arXiv 2017 | Interactive Video Object Segmentation in the Wild |
| ICML 2017 | Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs |
| arXiv 2017 | Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks |
| Transactions on Multimedia 2017 | AENet: Learning Deep Audio Features for Video
Analysis |
| ICCV 2017 | PathTrack: Fast Trajectory Annotation with Path Supervision |