Search Results for author: Domen Tabernik

Found 7 papers, 6 papers with code

Mixed supervision for surface-defect detection: from weakly to fully supervised learning

2 code implementations13 Apr 2021 Jakob Božič, Domen Tabernik, Danijel Skočaj

We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.

Anomaly Detection Defect Detection +1

End-to-end training of a two-stage neural network for defect detection

1 code implementation15 Jul 2020 Jakob Božič, Domen Tabernik, Danijel Skočaj

We demonstrate the performance of the end-to-end training scheme and the proposed extensions on three defect detection datasets - DAGM, KolektorSDD and Severstal Steel defect dataset - where we show state-of-the-art results.

 Ranked #1 on Defect Detection on DAGM2007 (Average Precision metric)

Defect Detection Segmentation

Segmentation-Based Deep-Learning Approach for Surface-Defect Detection

5 code implementations20 Mar 2019 Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj

This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.

Anomaly Detection Defect Detection

Spatially-Adaptive Filter Units for Deep Neural Networks

2 code implementations CVPR 2018 Domen Tabernik, Matej Kristan, Aleš Leonardis

Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters.

Image Classification Semantic Segmentation

Towards Deep Compositional Networks

no code implementations13 Sep 2016 Domen Tabernik, Matej Kristan, Jeremy L. Wyatt, Aleš Leonardis

We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function.

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