no code implementations • 18 May 2024 • Duo Peng, Qiuhong Ke, Jun Liu
Text-to-Image (T2I) models have raised security concerns due to their potential to generate inappropriate or harmful images.
no code implementations • 26 Aug 2023 • Duo Peng, Qiuhong Ke, Yinjie Lei, Jun Liu
Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain.
no code implementations • ICCV 2023 • Duo Peng, Ping Hu, Qiuhong Ke, Jun Liu
Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS).
1 code implementation • CVPR 2022 • Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li
In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.
no code implementations • 5 Aug 2021 • Duo Peng, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Jun Liu
In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.
1 code implementation • ICCV 2021 • Duo Peng, Yinjie Lei, Wen Li, Pingping Zhang, Yulan Guo
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain.
no code implementations • 19 Oct 2020 • Yinjie Lei, Duo Peng, Pingping Zhang, Qiuhong Ke, Haifeng Li
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.