Sander Riisøen Jyhne

• Jyhne, Sander; Andersen, Per-Arne; Goodwin, Morten & Oveland, Ivar (2023). A Contrastive Learning Scheme with Transformer Innate Patches. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, p. 103–114. doi: 10.1007/978-3-031-47994-6_8. Show summary

  This paper presents Contrastive Transformer (CT), a contrastive learning scheme using the innate transformer patches. CT enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all patch-based vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is treated as an image. We apply and test CT for the case of aerial image segmentation, known for low-resolution data, large class imbalance, and similar semantic classes. We perform extensive experiments to show the efficacy of the CT scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different transformer architectures. Ultimately, the results show a consistent increase in mean Intersection-over-Union (IoU) across all classes.

• Jyhne, Sander; Jacobsen, Jørgen Åsbu; Goodwin, Morten & Andersen, Per-Arne (2023). DeNISE: Deep Networks for Improved Segmentation Edges. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. 675, p. 81–89. doi: 10.1007/978-3-031-34111-3_8. Show summary

  This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%.

• Jyhne, Sander; Goodwin, Morten; Andersen, Per-Arne; Oveland, Ivar; Nossum, Alexander Salveson & Ormseth, Karianne Øydegard [Show all 8 contributors for this article] (2022). MapAI: Precision in BuildingSegmentation. Nordic Machine Intelligence (NMI). ISSN 2703-9196. 2, p. 1–3. doi: 10.5617/nmi.9849. Full text in Research Archive Show summary

  MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) 1 in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR)2 , the Norwegian Mapping Authority3 , AI:Hub4 , Norkart5 , and the Danish Agency for Data Supply and Infrastructure6 . The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images. Furthermore, we use IoU and Boundary IoU [1] to properly evaluate the precision of the models, with the latter being an IoU measure that evaluates the results’ boundaries. We provide the participants with a training dataset and keep a test dataset for evaluation.

• Saha, Rupsa & Jyhne, Sander (2022). Interpretable Text Classification in Legal Contract Documents using Tsetlin Machines. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. p. 7–12. doi: 10.1109/ISTM54910.2022.00011. Show summary

  Legal text contains various challenges in automated processing, compounded by the lack of detailed resources available for them. However, the ability of process such texts automatically is highly sought after. In this paper we try to parse a set of contract documents and identify key legal terminologies present in them, with the help of four text processing methods from different backgrounds : Tsetlin Machines, BERT, CNNBiLSTM and FastText. We show that the TM based approach works at par with other popular methods, with the added benefit of making available important clause literals that can act as specific linguistic cues to legal terminology.

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