An End-to-End Haptic Adjective Recognition Method with Self-Attention Mechanism
文献类型：会议论文
| 作者 | Yuanpei Zhang2,3 ; Zhuojun Zou1,2; Lin Shu1,2; Jie Hao1,2
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| 出版日期 | 2023-10 |
| 会议日期 | October 28-29, 2023 |
| 会议地点 | Wuhan, China |
| 关键词 | Haptic Adjective Recognition End-to-End Framework |
| 英文摘要 | Human's ability to describe the tactile sensation of objects has piqued the interest of numerous researchers seeking to augment the dexterity of robots in delicate tasks. However, most existing approaches are limited by their two-stage framework, resulting in low inference efficiency and unsatisfactory performance. To address this challenge, we propose the first end-to-end framework for haptic adjective classification. Specifically, our framework leverages the Space Encoding Module to capture long-term dependencies, and the Order Encoding Module to learn order information explicitly. We conduct experiments on the public PHAC-2 Dataset and the result shows that our method achieves F1 score of 0.759, outperforming previous work in a significant way. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56579]  |
| 专题 | 国家专用集成电路设计工程技术研究中心_实感计算 |
| 通讯作者 | Jie Hao |
| 作者单位 | 1.Guangdong Institute of Artificial Intelligence and Advanced Computing, Guangzhou, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Yuanpei Zhang,Zhuojun Zou,Lin Shu,et al. An End-to-End Haptic Adjective Recognition Method with Self-Attention Mechanism[C]. 见:. Wuhan, China. October 28-29, 2023. |
入库方式： OAI收割
来源：自动化研究所
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