%0 Journal Article %T Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification %A Joakim Bruslund Haurum %A Meysam Madadi %A Sergio Escalera %A Thomas B. Moeslund %J Automation in Construction %D 2022 %V 144 %F Joakim Bruslund Haurum2022 %O HuPBA;MILAB %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3780), last updated on Tue, 25 Apr 2023 15:26:25 +0200 %X A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points. %K Sewer Defect Classification %K Vision Transformers %K Sinkhorn-Knopp %K Convolutional Neural Networks %K Closed-Circuit Television %K Sewer Inspection %U http://dx.doi.org/10.1016/j.autcon.2022.104614 %P 104614