Dr Simone Schaub-Meyer (TU Darmstadt): Efficient and Understandable Neural Networks for Image and Video Analysis

21 March 2024, 14-15 h in H-C 7327

Recent developments in deep learning have led to significant advances in many areas of computer vision. However, the success of these methods often depends on having a well-defined task corresponding training data, and measuring success by improved task specific accuracy. However, in order to apply new methods in the real world, other aspects become relevant as well, such as required labelled data, computational requirements, as well as, especially in safety critical scenarios, how trust worthy a model is.

In my talk, I will first discuss how motion in videos can be used to learn representations in an unsupervised way as well as methods to efficiently handle higher resolution data. In the second part, I will show how attribution maps, which help to gain a better understanding of the predictions, can be obtained efficiently, as well as how we can evaluate them.