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May 14, 2018
Talk 1
Speaker
Michal Koperski (STARS)
Title
Spatio-Temporal Features for Action Recognition
Abstract
Advancements in Convolutional Neural Networks showed significant improvement in object detection and recognition task on still images. But in case of video analysis, the improvement is less significant and shallow architectures with handcrafted features still obtain competitive results. It seems that video analysis requires different representation. This talk will cover action recognition problem, we will focus on spatio-temporal features modeling. We will review how this problem is tackled by state-of-the-art methods including shallow and deep architectures. Finally, we will show our methods which efficiently model spatio-temporal layout of features and improve state-of-the-art results.
Talk 2
Speaker
Théo Thonat (GRAPHDECO)
Title
Real time Image Based Rendering
Abstract
Creating realistic images with the traditional rendering pipeline requires tedious manual work, from 3D models generation to lighting setup. Image Based Rendering is an alternative way to render a realistic scene, only using a set of casually captured photos.
In this talk, I will present the principles of image based rendering and some implementations that have been proposed over the past few years, focusing on the real time approaches. I will then present their current limitations and how some of our team projects are trying to overcome them.
