Dual-Structure Disentangling Variational Generation for Data-Limited Face Parsing | |
Li PP(李佩佩) | |
2021 | |
会议名称 | ACM International Conference on Multimedia |
会议日期 | 2021-10 |
会议地点 | online |
摘要 | Deep learning based face parsing methods have attained state-of-the-art performance in recent years. Their superior performance heavily depends on the large-scale annotated training data. However, it is expensive and time-consuming to construct a large-scale pixel-level manually annotated dataset for face parsing. To alleviate this issue, we propose a novel Dual-Structure Disentangling Variational Generation (D2VG) network. Benefiting from the interpretable factorized latent disentanglement in VAE, D2VG can learn a joint structural distribution of facial image and its corresponding parsing map. Owing to these, it can synthesize large-scale paired face images and parsing maps from a standard Gaussian distribution. Then, we adopt both manually annotated and synthesized data to train a face parsing model in a supervised way. Since there are inaccurate pixel-level labels in synthesized parsing maps, we introduce a coarseness-tolerant learning algorithm, to effectively handle these noisy or uncertain labels. In this way, we can significantly boost the performance of face parsing. Extensive quantitative and qualitative results on HELEN, CelebAMask-HQ and LaPa demonstrate the superiority of our methods. |
七大方向——子方向分类 | 生物特征识别 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44789 |
专题 | 智能感知与计算研究中心 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li PP. Dual-Structure Disentangling Variational Generation for Data-Limited Face Parsing[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Dual-StructureDisent(2431KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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