Visual tactile fusion object clustering
文献类型：会议论文
| 作者 | Zhang T(张涛)1,4 ; Cong Y(丛杨)1; Sun G(孙干)1,4; Wang QQ(王倩倩)3; Ding ZM(丁正明)2
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| 出版日期 | 2020 |
| 会议日期 | February 7-12, 2020 |
| 会议地点 | New York |
| 页码 | 10426-10433 |
| 英文摘要 | Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensively-studied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align the visual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework. |
| 源文献作者 | Association for the Advancement of Artificial Intelligence |
| 产权排序 | 1 |
| 会议录 | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
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| 会议录出版者 | AAAI press |
| 会议录出版地 | Palo Alto, CA |
| 语种 | 英语 |
| ISBN号 | 978-1-5773-5835-0 |
| WOS记录号 | WOS:000668126802105 |
| 源URL | [http://ir.sia.cn/handle/173321/28933]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Cong Y(丛杨) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 2.Indiana University-Purdue University, Indianapolis, United States 3.Xidian University, China 4.University of Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Zhang T,Cong Y,Sun G,et al. Visual tactile fusion object clustering[C]. 见:. New York. February 7-12, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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