Domain Adaptation With Foreground/Background Cues and Gated Discriminators

Yong-Xiang Lin*, Daniel Stanley Tan, Yung-Yao Chen, Ching-Chun Huang, Kai-Lung Hua

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that background objects tend to be similar across domains, while foreground objects tend to have more variations. Using this insight, we propose an adaptation method that uses foreground and background cues and adapt them separately. We also propose a mask-aware gated discriminator that learns soft masks from the input foreground and background masks instead of naively performing binary masking that immediately removes information outside of the predicted masks. We evaluate our method on two different datasets and show that our method outperforms several state-of-the-art baselines, which verifies the effectiveness of our approach.

    Original languageEnglish
    Article number9138693
    Pages (from-to)44-53
    Number of pages10
    JournalIEEE Multimedia
    Volume27
    Issue number3
    DOIs
    StatePublished - Jul 2020

    Keywords

    • Image segmentation
    • Semantics
    • Adaptation models
    • Logic gates
    • Automobiles
    • Training data
    • Computer science
    • Autonmous automobiles

    Fingerprint

    Dive into the research topics of 'Domain Adaptation With Foreground/Background Cues and Gated Discriminators'. Together they form a unique fingerprint.

    Cite this