{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:24Z","timestamp":1750220064079,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":14,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Scientific Research Foundation for the Talents of USTC, the National Natural Science Foundation of China","award":["61971393,61871361"],"award-info":[{"award-number":["61971393,61871361"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,23]]},"DOI":"10.1145\/3578741.3578819","type":"proceedings-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T04:18:52Z","timestamp":1678162732000},"page":"372-376","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Grasp stability assessment through the fusion of visual and tactile signals using deep bilinear network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-0822","authenticated-orcid":false,"given":"Tao","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2864-349X","authenticated-orcid":false,"given":"Yin","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8447-9615","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5788-894X","authenticated-orcid":false,"given":"Minghui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9910-8967","authenticated-orcid":false,"given":"Ao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat8414"},{"key":"e_1_3_2_1_2_1","volume-title":"Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International journal of robotics research","author":"Levine S","year":"2018","unstructured":"Levine S , Pastor P , Krizhevsky A , Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International journal of robotics research , 2018 , 37(4-5): 421-436. Levine S, Pastor P, Krizhevsky A, Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International journal of robotics research, 2018, 37(4-5): 421-436."},{"key":"e_1_3_2_1_3_1","first-page":"11443","article-title":"Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots[C]\/\/2021 IEEE International Conference on Robotics and Automation (ICRA)","volume":"2021","author":"Yoo Y","unstructured":"Yoo Y , Lee C Y , Zhang B T . Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots[C]\/\/2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE , 2021 : 11443 - 11449 . Yoo Y, Lee C Y, Zhang B T. Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots[C]\/\/2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021: 11443-11449.","journal-title":"IEEE"},{"key":"e_1_3_2_1_4_1","first-page":"1563","article-title":"Predict robot grasp outcomes based on multi-modal information[C]\/\/2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"2018","author":"Yang C","unstructured":"Yang C , Du P , Sun F , Predict robot grasp outcomes based on multi-modal information[C]\/\/2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) . IEEE , 2018 : 1563 - 1568 . Yang C, Du P, Sun F, Predict robot grasp outcomes based on multi-modal information[C]\/\/2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2018: 1563-1568.","journal-title":"IEEE"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364907087172"},{"key":"e_1_3_2_1_6_1","first-page":"2238","article-title":"Grasp stability assessment through unsupervised feature learning of tactile images[C]\/\/2017 IEEE International Conference on Robotics and Automation (ICRA)","volume":"2017","author":"Cockbum D","unstructured":"Cockbum D , Roberge J P , Maslyczyk A , Grasp stability assessment through unsupervised feature learning of tactile images[C]\/\/2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE , 2017 : 2238 - 2244 . Cockbum D, Roberge J P, Maslyczyk A, Grasp stability assessment through unsupervised feature learning of tactile images[C]\/\/2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 2238-2244.","journal-title":"IEEE"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2852779"},{"key":"e_1_3_2_1_8_1","first-page":"2470","article-title":"Learning to grasp using visual information[C]\/\/Proceedings of IEEE International Conference on Robotics and Automation","volume":"3","author":"Kamon I","year":"1996","unstructured":"Kamon I , Flash T , Edelman S . Learning to grasp using visual information[C]\/\/Proceedings of IEEE International Conference on Robotics and Automation . IEEE , 1996 , 3 : 2470 - 2476 . Kamon I, Flash T, Edelman S. Learning to grasp using visual information[C]\/\/Proceedings of IEEE International Conference on Robotics and Automation. IEEE, 1996, 3: 2470-2476.","journal-title":"IEEE"},{"key":"e_1_3_2_1_9_1","volume-title":"Grasp state assessment of deformable objects using visual-tactile fusion perception[C]\/\/2020 IEEE International Conference on Robotics and Automation (ICRA)","author":"Cui S","year":"2020","unstructured":"Cui S , Wang R , Wei J , Grasp state assessment of deformable objects using visual-tactile fusion perception[C]\/\/2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE , 2020 : 538-544. Cui S, Wang R, Wei J, Grasp state assessment of deformable objects using visual-tactile fusion perception[C]\/\/2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 538-544."},{"key":"e_1_3_2_1_10_1","volume-title":"Learning grasp stability based on tactile data and HMMs[C]\/\/19th International Symposium in Robot and Human Interactive Communication","author":"Bekiroglu Y","year":"2010","unstructured":"Bekiroglu Y , Kragic D , Kyrki V. Learning grasp stability based on tactile data and HMMs[C]\/\/19th International Symposium in Robot and Human Interactive Communication . IEEE , 2010 : 132-137. Bekiroglu Y, Kragic D, Kyrki V. Learning grasp stability based on tactile data and HMMs[C]\/\/19th International Symposium in Robot and Human Interactive Communication. IEEE, 2010: 132-137."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep learning for tactile understanding from visual and haptic data[C]\/\/2016 IEEE International Conference on Robotics and Automation (ICRA)","author":"Gao Y","year":"2016","unstructured":"Gao Y , Hendricks L A , Kuchenbecker K J , Deep learning for tactile understanding from visual and haptic data[C]\/\/2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE , 2016 : 536-543. Gao Y, Hendricks L A, Kuchenbecker K J, Deep learning for tactile understanding from visual and haptic data[C]\/\/2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 536-543."},{"key":"e_1_3_2_1_12_1","volume-title":"Multimodal keyless attention fusion for video classification[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence","author":"Long X","year":"2018","unstructured":"Long X , Gan C , Melo G , Multimodal keyless attention fusion for video classification[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence . 2018 , 32(1). Long X, Gan C, Melo G, Multimodal keyless attention fusion for video classification[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1)."},{"key":"e_1_3_2_1_13_1","volume-title":"Audiovisual slowfast networks for video recognition[J]. arXiv preprint arXiv:2001.08740","author":"Xiao F","year":"2020","unstructured":"Xiao F , Lee Y J , Grauman K , Audiovisual slowfast networks for video recognition[J]. arXiv preprint arXiv:2001.08740 , 2020 . Xiao F, Lee Y J, Grauman K, Audiovisual slowfast networks for video recognition[J]. arXiv preprint arXiv:2001.08740, 2020."},{"key":"e_1_3_2_1_14_1","volume-title":"Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis[J]. Cancer research","author":"Cheng J","year":"2017","unstructured":"Cheng J , Zhang J , Han Y , Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis[J]. Cancer research , 2017 , 77(21): e91-e100. Cheng J, Zhang J, Han Y, Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis[J]. Cancer research, 2017, 77(21): e91-e100."}],"event":{"name":"MLNLP 2022: 2022 5th International Conference on Machine Learning and Natural Language Processing","acronym":"MLNLP 2022","location":"Sanya China"},"container-title":["Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578741.3578819","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3578741.3578819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:51Z","timestamp":1750183731000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578741.3578819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":14,"alternative-id":["10.1145\/3578741.3578819","10.1145\/3578741"],"URL":"https:\/\/doi.org\/10.1145\/3578741.3578819","relation":{},"subject":[],"published":{"date-parts":[[2022,12,23]]},"assertion":[{"value":"2023-03-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}