Visual Intelligence Seminar series

Every two weeks Visual Intelligence hosts a seminar with invited speakers. The seminars cover relevant topics involving image analysis or deep learning research within our innovation areas. The speakers are usually researchers from within the organization, from or user partners or external presenters. All seminars are recorded and shared on our Youtube-channel.

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Next VI Seminar will be announced shortly.

Previous VI seminars

Automated building detection from airborne hyperspectral and lidar data

May 27, 2021
VI Seminar 2021 #7

Urban maps in Norway are currently updated using manual photo interpretation on stereo aerial imagery. However, there is often a substantial delay after completion of construction work until new buildings, roads, etc. appear in updated versions of the urban maps. Automated pixel-based urban land cover classification from multispectral aerial images of very high resolution has proven difficult since the same spectral values may occur within several land cover types. Airborne hyperspectral data may provide better discriminative power. However, there is still the problem that the same types of material may exist within different land cover types, such as buildings, roads, parks, gardens, etc. In this seminar Øyvind Trier dives into how these challenges can be assessed using deep learning.

Designing deep learning studies in medical diagnostics and beyond

April 15, 2021
VI Seminar 2021 #5

Many deep learning studies are not designed to provide unbiased estimation of the system's performance in the intended application. Reports of overoptimistic estimates and opportunities may inflate the expectation of what is currently possible, misguide resource allocation, and hamper the progression of the field. In this talk, we will look into how the performance of a deep learning system in an intended application could be estimated more reliably than what is currently common practice, even if restricted to using retrospective data.

Using Neural Networks for Satellite Based Maritime Monitoring

March 18, 2021
VI Seminar 2021 #4

The recent advances in deep learning and drastic increase in number of imaging satellites, new levels of automation are possible and necessary. KSAT is investing significantly into modern MLOps practices to achieve this and intends to use its membership in Visual Intelligence to solve the research aspects of this transformation.

Andrew Gilbert: Generating Synthetic Labeled Data from Existing Anatomical Models

March 4, 2021
VI Seminar 2021 #3

Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and transformed into realistic synthetic ultrasound images with CycleGAN.

Olav Brautaset: Marine Acoustic Classification: Semantic Segmentation of Echosounder Data using CNNs

February 18, 2021
VI Seminar 2021 #2

In fisheries acoustics, echo sounding is applied to detect fish and other marine objects in the ocean; a central tool for stock assessments and establishing fishing quotas. Fish detection and species classification from echo sounder data is typically a manual process. In our work, we automate this process by training a convolutional neural network for semantic segmentation using supervised learning. The talk will describe the data, the CNN-approach used for segmentation – as well as issues related to the training data, such as the quality of annotations when used in a machine learning setting.

Kristoffer Wickstrøm: Recent advances in explainable deep learning and how to model uncertainty in explainability

February 4, 2021
VI Seminar 2021 #1

Deep learning is the cornerstone of artificial intelligence applications across a wide range of tasks and domains. An important component that is missing from deep learning is explainability, i.e. the ability to explain what influenced a prediction made by a deep learning-based system. Explainable deep learning is an active area of research, with new algorithms being proposed at a rapid pace. This presentation highlights existing methods for explainable deep learning, as well as how to model uncertainty in explainability.