Deep learning for continuous multiple time series annotations | |
Huang, Jian2,3; Li, Ya3; Tao, Jianhua1,2,3; Lian, Zheng2,3; Niu, Mingyu2,3; Yang, Minghao3 | |
2018-10 | |
会议日期 | 2018.10.22-2018.10.26 |
会议地点 | Seoul, Republic of Korea |
英文摘要 | 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 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39303] |
专题 | 模式识别国家重点实验室_智能交互 |
作者单位 | 1.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
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]. 见:. Seoul, Republic of Korea. 2018.10.22-2018.10.26. |
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