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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation (CVPR 2019)
18.04.2019 10:00-11:45
Speaker: Patrick Pérez
Title: ADVENT: Adversarial Entropy Minimization for Domain Adaptation in
Semantic Segmentation (CVPR 2019)
Abstract: Semantic segmentation is a key problem for many computer vision
tasks.
While approaches based on convolutional neural networks constantly break new
records on different benchmarks, generalizing well to diverse testing
environments remains a major challenge. In numerous real world applications,
there is indeed a large gap between data distributions in train and test
domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we
propose two novel, complementary methods using (i) entropy loss and (ii)
adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging “synthetic-2-real” set-ups and show that the approach can also be used for detection.
Patrick Pérez is Scientific Director of valeo.ai, a Valeo AI research lab
focused on self-driving cars.
https://ptrckprz.github.io
- Místo konání
- E-112 (Vyčichlova knihovna), FEL ČVUT, Karlovo náměstí 13, Praha 2
- Pořadatel
- Katedra kybernetiky FEL ČVUT
- Kontaktní osoba
- doc. Ing. Tomáš Svoboda, Ph.D., svobodat@fel.cvut.cz, 224 357 448
- Podrobnější informace
- https://cyber.felk.cvut.cz/seminars/?event=1301