Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain.
In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.
To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions.
Convolutional neural networks have been successfully applied to semantic segmentation problems.
Ranked #8 on Lane Detection on TuSimple
We present an interactive segmentation approach for liver tumors in US acquisitions.
no code implementations • 21 Oct 2015 • Jan Egger, Harald Busse, Philipp Brandmaier, Daniel Seider, Matthias Gawlitza, Steffen Strocka, Philip Voglreiter, Mark Dokter, Michael Hofmann, Bernhard Kainz, Alexander Hann, Xiaojun Chen, Tuomas Alhonnoro, Mika Pollari, Dieter Schmalstieg, Michael Moche
The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.