no code implementations • 28 Dec 2021 • Qiqi Gu, Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Ran Yi
The progressive enhancement process facilitates the learning of discriminative features with fine-grained face forgery clues.
1 code implementation • ICCV 2021 • Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma
Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.
1 code implementation • 8 Aug 2021 • Qianyu Zhou, Zhengyang Feng, Qiqi Gu, Jiangmiao Pang, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma
The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
Ranked #5 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
no code implementations • 8 Aug 2021 • Qianyu Zhou, Qiqi Gu, Jiangmiao Pang, Xuequan Lu, Lizhuang Ma
In this paper, we study a practical setting called Specific Domain Adaptation (SDA) that aligns the source and target domains in a demanded-specific dimension.
Image-to-Image Translation on Cityscapes-to-Foggy Cityscapes object-detection +3
no code implementations • 19 Apr 2020 • Qianyu Zhou, Zhengyang Feng, Qiqi Gu, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma
Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner.
1 code implementation • 18 Apr 2020 • Zhengyang Feng, Qianyu Zhou, Qiqi Gu, Xin Tan, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.