Our guest speaker is Yuki Asano from the University of Amsterdam.

You are all cordially invited to the CVG Seminar on the 9th of December at 2:30 p.m. CET

• via Zoom (passcode is 303207).

Abstract

It is a talk about pushing the limits of what can be learnt without using any human annotations. After a first overview of what self-supervised learning is, we will first dive into how clustering can be combined with representation learning using optimal transport and how this can be leveraged to unsupervisedly segment objects in images [1]. Finally, as augmentations are crucial for all of the self-supervised learning, we will analyze these in more detail in a recent preprint [2]. Here, we show that it is possible to extrapolate to semantic classes such as those of ImageNet using just a single datum as visual input when combined with strong augmentations.

[1] Self-Supervised Learning of Object Parts for Semantic Segmentation [arxiv]

[2] Extrapolating from a Single Image to a Thousand Classes using Distillation [arxiv]

Bio

Yuki Asano is an assistant professor for computer vision and machine learning at the Qualcomm-UvA lab at the University of Amsterdam, where he works with Cees Snoek, Max Welling and Efstratios Gavves. His current research interests are multi-modal and self-supervised learning and ethics in computer vision. Prior to his current appointment, he finished his PhD at the Visual Geometry Group (VGG) at the University of Oxford working with Andrea Vedaldi and Christian Rupprecht. During his time as a PhD student, he also interned at Facebook AI Research and worked at TransferWise. Prior to the PhD, he studied physics at the University of Munich (LMU) and Economics in Hagen as well as a MSc in Mathematical Modelling and Scientific Computing at the Mathematical Institute in Oxford.

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.