Knowledge Commons of Institute of Automation,CAS
Deep learning for continuous multiple time series annotations | |
Huang, Jian1,3; Li, Ya1; Tao, Jianhua1,2,3; Lian, Zheng1,3; Niu, Mingyu1,3; Yang, Minghao1 | |
2018-10 | |
会议名称 | Proceedings of the 2018 on Audio/Visual Emotion Challenge and Workshop, ACM 2018 |
会议日期 | 2018.10.22-2018.10.26 |
会议地点 | Seoul, Republic of Korea |
摘要 | Learning from multiple annotations is an increasingly important research topic. Compared with conventional classification or regression problems, it faces more challenges because time-continuous annotations would result in noisy and temporal lags problems for continuous emotion recognition. In this paper, we address the problem by deep learning for continuous multiple time series annotations. We attach a novel crowd layer to the output layer of basic continuous emotion recognition system, which learns directly from the noisy labels of multiple annotators with end-to-end manner. The inputs of the system are multimodal features and the targets are multiple annotations, with the intention of learning an annotator-specific mapping. Our proposed method considers the ground truth as latent variables and multiple annotations are variant of ground truth by linear mapping. The experimental results show that our system can achieve superior performance and capture the reliabilities and biases of different annotators. |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39303 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Huang, Jian,Li, Ya,Tao, Jianhua,et al. Deep learning for continuous multiple time series annotations[C],2018. |
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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 | ||
AVEC2018-GES-huang.p(9760KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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