May 2021: “The developmental trajectory of object recognition robustness: comparing children, adults, and CNNs“, a project of my Master student, Lukas Huber, has been accepted as a talk at VSS 2021!
December 2020: Since our paper “On the surprising similarities of supervised and self-supervised models” was selected as “Oral” at the NeurIPS 2020 workshop on the Shared Visual Representations in Human & Machine Intelligence, I had the pleasure to talk about our work comparing human perception against networks trained with and without labels:
November 2020: Proud to have received a NeurIPS 2020 Outstanding Reviewer Award (top 10% of reviewers)
November 2020: “Shortcut learning in deep neural networks” has just been published by Nature Machine Intelligence!
May 2020: I am honoured to have been selected for an Elsevier/Vision Research Travel Award to attend the 2020 virtual meeting of the Vision Sciences Society
February 2020: Spektrum der Wissenschaft, the German edition of the Scientific American (popular science magazine), has printed an article featuring our work on shape vs. texture
July 2019: Our work has been featured by an article in Quanta Magazine: “Where We See Shapes, AI Sees Textures”
July 2019: I’ve attended the Computational Vision Summer School (CVSS) in Freudenstadt, Germany
May 2019: I’ve given a talk at VSS 2019 about “Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs”
May 2019: Hosted by Robbe Goris, I’ve visited UT Austin‘s Center for Perceptual Systems for a few days where I gave a talk about “Where humans still outperform Convolutional Neural Networks—and how to narrow the gap”
May 2019: I’ve given a talk at ICLR 2019 about “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”:
The talk is available here:
March 2019: I’ve been invited to give a talk at the AI Meetup Hamburg about “The (in)corrigible laziness of convolutional neural networks”