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Generative Partial Visual-Tactile Fused Object Clustering
Zhang T(张涛)1,2,3; Cong Y(丛杨)1; Sun G(孙干)1; Dong JH(董家华)1,2,3; Liu YY(刘宇阳)1,2,3; Ding ZM(丁正明)
Department机器人学研究室
Conference Name35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence
Conference DateFebuary 2-9, 2021
Conference PlaceELECTR NETWORK
Author of SourceAssociation for the Advancement of Artificial Intelligence
Source PublicationTHIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
PublisherAAAI
Publication PlacePalo Alto, California
2021
Pages6156-6164
Indexed ByCPCI(ISTP)
WOS IDWOS:000680423506030
Contribution Rank1
ISSN2159-5399
ISBN978-1-57735-866-4
AbstractVisual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering performance. However, the missing data (i.e., partial data) issues always happen due to occlusion and noises during the data collecting process. This issue is not well solved by most existing partial multi-view clustering methods for the heterogeneous modality challenge. Naively employing these methods would inevitably induce a negative effect and further hurt the performance. To solve the mentioned challenges, we propose a Generative Partial Visual-Tactile Fused (i.e., GPVTF) framework for object clustering. More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces. A conditional cross-modal clustering generative adversarial network is then developed to synthesize one modality conditioning on the other modality, which can compensate missing samples and align the visual and tactile modalities naturally by adversarial learning. To the end, two pseudo-label based KL-divergence losses are employed to update the corresponding modality-specific encoders. Extensive comparative experiments on three public visual-tactile datasets prove the effectiveness of our method.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/29555
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
2.Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, 4Department of Computer Science Tulane University, USA
Recommended Citation
GB/T 7714
Zhang T,Cong Y,Sun G,et al. Generative Partial Visual-Tactile Fused Object Clustering[C]//Association for the Advancement of Artificial Intelligence. Palo Alto, California:AAAI,2021:6156-6164.
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