Research Seminar


In this weekly seminar we present and discuss cutting edge research from the field of computer vision but also more general machine learning papers. Students (Bachelor or Master), PhD students and PostDocs are invited to join us. Each week one paper gets presented and discussed. All participants prepare the paper beforehand and take remarks to be discussed during the seminar.

Place and time

The reading group takes place virtually via Zoom every Friday from 11 am to 12 am. If you like to participate, please write an e-mail to Michael Oechsle, who will provide you with further details.

Date Title Speaker
16.04.2021 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Yiyi Liao
09.04.2021 Object-Centric Learning with Slot Attention Michael Niemeyer
02.04.2021 Learning to Simulate Stefan Baur
26.03.2021 Building Rome in a Day Carolin Schmitt
26.02.2021 The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Axel Saur
19.02.2021 Group Normalization Niklas Hanselmann
12.02.2021 LambdaNetworks: Modeling long-range Interactions without Attention



05.02.2021 Neural Tangent Kernel: Convergence and Generalization in Neural Networks Katja Schwarz
29.01.2021 Invited Talk: Efficient Transformers Angelos Katharopoulos
22.01.2021 Spatial Transformer Networks Songyou Peng
15.01.2021 What Do Single-view 3D Reconstruction Networks Learn?



08.01.2021 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Aditya Prakash
18.12.2020 SinGAN: Learning a Generative Model from a Single Natural Image Christian Reiser
11.12.2020 Deep Equilibrium Models Michael Oechsle
27.11.2020 CVPR Submissions  
30.10.2020 Solving Rubik’s Cube with a Robot Hand Joo Ho Lee
09.10.2020 Secrets of Optical Flow Estimation and Their Principles Stefan Baur
02.10.2020 Accurate, Dense, and Robust Multi-View Stereopsis Carolin Schmitt
25.09.2020 Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Michael Oechsle
18.09.2020 Neural Processes Yiyi Liao
11.09.2020 Relational Inductive Biases, Deep Learning and Graph Networks, DeepMind Aditya Prakash


Your classifier is secretly an energy-based model and you should treat it like one Niklas Hanselmann