You are all cordially invited to the Master Thesis defence on the 27th of October at 11 a.m. CEST

• in person at the Institute of Computer Science: room 302, Neubrückstrasse 10, 3012 Bern
• via Zoom (passcode is 327699).

Abstract

Depression is the second major cause for years spent in disability and has a growing prevalence in adolescents. The recent Covid-19 pandemic has intensified the situation and limited in-person patient monitoring due to distancing measures. Recent advances in wearable devices have made it possible to record the rest/activity cycle remotely with high precision and in real-world contexts. We aim to use machine learning methods to predict an individual's mental health based on wearable-measured sleep and physical activity. Predicting an impending mental health crisis of an adolescent allows for prompt intervention,  detection of depression onset or its recursion, and remote monitoring. To achieve this goal, we train three primary forecasting models; linear regression, random forest, and light gradient boosted machine (LightGBM); and two deep learning models; block recurrent neural network (block RNN) and temporal convolutional network (TCN); on Actigraph measurements to forecast mental health in terms of depression, anxiety, sleepiness, stress, sleep quality, and behavioural problems. Our models achieve a high forecasting performance, the random forest being the winner to reach an accuracy of 98% for forecasting the trait anxiety. We perform extensive experiments to evaluate the models' performance in accuracy, generalization, and feature utilization, using a naive forecaster as the baseline. Our analysis shows minimal mental health changes over two months, making the prediction task easily achievable. Due to these minimal changes in mental health, the models tend to primarily use the historical values of mental health evaluation instead of Actigraph features. At the time of this master thesis, the data acquisition step is still in progress. In future work, we plan to train the models on the complete dataset using a longer forecasting horizon to increase the level of mental health changes and perform transfer learning to compensate for the small dataset size. This interdisciplinary project demonstrates the opportunities and challenges in machine learning-based prediction of mental health, paving the way toward using the same techniques to forecast other mental disorders such as internalizing disorder, Parkinson's disease, Alzheimer's disease, etc. and improving the quality of life for individuals who have some mental disorder.

You are all cordially invited to the Bachelor Thesis defence on the 23rd of September at 2:30 p.m. CEST

• in person at the Institute of Computer Science: room 302, Neubrückstrasse 10, 3012 Bern
• via Zoom (passcode is 871438).

Abstract

The calculation of variables of brain morphology is computationally very expensive and time-consuming. Previous work showed the feasibility of extracting the variables directly from T1-weighted brain MRI images using a convolutional neural network. We used significantly more data and extended their model to a new set of neuromorphological variables, which could become interesting biomarkers in the future for the diagnosis of brain diseases. The model shows for nearly all subjects a less than 5% mean relative absolute error. This high relative accuracy can be attributed to the low morphological variance between subjects and the ability of the model to predict the cortical atrophy age trend. The model however fails to capture all the variance in the data and shows large regional differences. We attribute these limitations in part to the moderate to poor reliability of the ground truth generated by FreeSurfer. We further investigated the effects of training data size and model complexity on this regression task and found that the size of the dataset had a significant impact on performance, while deeper models did not perform better. Lack of interpretability and dependence on a silver ground truth are the main drawbacks of this direct regression approach.

You are all cordially invited to the Master Thesis defence on the 9th of September at 2:30 p.m. CEST

• in person at the Institute of Computer Science: room 302, Neubrückstrasse 10, 3012 Bern
• via Zoom (passcode is 678646).

Abstract

The use of automated systems describing human motion has become possible in various domains. Most of the proposed systems are designed to work with people moving around in a standing position. Because such a system could be interesting in a medical environment, we propose in this work a pipeline that can effectively predict human motion from people lying on beds. The proposed pipeline is tested with a data set composed of 41 participants executing 7 predefined tasks in a bed. The motion of the participants is measured with video cameras, accelerometers and a pressure mat. Various experiments are carried out with the information retrieved from the data set. Two approaches combining the data from the different measurement technologies are explored. The performance of the different carried experiments is measured, and the proposed pipeline is composed of components providing the best results. Later on, we show that the proposed pipeline only needs to use video cameras, which makes the proposed environment easier to implement in real-life situations.

Our guest speaker is Katja Schwarz from the University of Tuebingen.

You are all cordially invited to the CVG Seminar on September 2nd at 2:30 p.m. CEST

• in person at the Institute of Computer Science: room 302, Neubrückstrasse 10, 3012 Bern
• via Zoom (passcode is 464482).

Abstract

What can we learn from 2D images? While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Recently, 3D-aware GANs have enabled explicit control over the camera pose and the generated content while training on 2D images, only. However, state-of-the-art 3D-aware generative models rely on coordinate-based MLPs which need to be queried for each sample along a camera ray, making volume rendering slow. Motivated by recent results in voxel-based novel view synthesis, I will introduce a sparse voxel grid representation for fast and 3D-consistent generative modeling in the first part of the talk.

In the second part, we will dive deeper into 2D GANs and investigate which spectral properties are learned from 2D images. Surprisingly, multiple recent works report an elevated amount of high frequencies in the spectral statistics which makes it straightforward to distinguish real and generated images. Explanations for this phenomenon are controversial: While most works attribute the artifacts to the generator, other works point to the discriminator. I will present our study on the frequency bias of generative models that takes a sober look at those explanations and provides insights on what makes proposed measures against high-frequency artifacts effective.

Bio

Katja is a 4th-year PhD student in the Autonomous Vision Group at Tuebingen University and is currently doing an internship with Sanja Fidler at NVIDIA. Katja received her BSc degree in 2016 and MSc degree in 2018 from Heidelberg University. In July 2019 she started her PhD at Tuebingen University under the supervision of Andreas Geiger. Her research lies at the intersection of computer vision and graphics and focuses on generative modeling in 2D and 3D.

Our guest speaker is Thomas Kipf from Google Brain and you are all cordially invited to the CVG Seminar on July 22nd at 2:30 p.m. CET on Zoom (passcode is 809285).

Abstract

The world around us — and our understanding of it — is rich in compositional structure: from atoms and their interactions to objects and entities in our environments. How can we learn models of the world that take this structure into account and generalize to new compositions in systematic ways? This talk focuses on an emerging class of slot-based neural architectures that utilize attention mechanisms to perform perceptual grouping of scenes into objects and abstract entities without direct supervision.
I will briefly introduce the Slot Attention mechanism as a core representative for this class of models and cover our recent extension of Slot Attention to multi-object video (SAVi). I will further give an overview of our new work on 1) extending SAVi to real-world video on the Waymo Open dataset (SAVi++), and 2) using Slot Attention in a scene representation transformer architecture to radically speed up 3D-centric object discovery via novel view synthesis (Object Scene Representation Transformer, OSRT).

Bio

Thomas is a Senior Research Scientist at Google Brain. He obtained his Ph.D. at the University of Amsterdam working with Max Welling. For his Ph.D. thesis on Deep Learning with Graph-Structured Representations, he received the ELLIS Ph.D. Award 2021. He is broadly interested in developing and studying machine learning models that can reason about the rich structure of both the physical and digital world and its combinatorial complexity. This includes topics in graph representation learning, object-centric learning, and causal representation learning.