Adversarial Semantic Hallucination for Domain Generalized Semantic
Segmentation
- URL: http://arxiv.org/abs/2106.04144v1
- Date: Tue, 8 Jun 2021 07:07:45 GMT
- Title: Adversarial Semantic Hallucination for Domain Generalized Semantic
Segmentation
- Authors: Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, Rick Siow Mong Goh
- Abstract summary: We propose an adversarial hallucination approach, which combines a class-wise hallucination module and a semantic segmentation module.
Experiments on state of the art domain adaptation work demonstrate the efficacy of our proposed method when no target domain data are available for training.
- Score: 50.14933487082085
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Convolutional neural networks may perform poorly when the test and train data
are from different domains. While this problem can be mitigated by using the
target domain data to align the source and target domain feature
representations, the target domain data may be unavailable due to privacy
concerns. Consequently, there is a need for methods that generalize well
without access to target domain data during training. In this work, we propose
an adversarial hallucination approach, which combines a class-wise
hallucination module and a semantic segmentation module. Since the segmentation
performance varies across different classes, we design a semantic-conditioned
style hallucination layer to adaptively stylize each class. The classwise
stylization parameters are generated from the semantic knowledge in the
segmentation probability maps of the source domain image. Both modules compete
adversarially, with the hallucination module generating increasingly
'difficult' style images to challenge the segmentation module. In response, the
segmentation module improves its performance as it is trained with generated
samples at an appropriate class-wise difficulty level. Experiments on state of
the art domain adaptation work demonstrate the efficacy of our proposed method
when no target domain data are available for training.
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