{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T22:52:44Z","timestamp":1776725564481,"version":"3.51.2"},"reference-count":98,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:00:00Z","timestamp":1584489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["IIS-1924928, IIS-1938167"],"award-info":[{"award-number":["IIS-1924928, IIS-1938167"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000144","name":"Division of Computer and Network Systems","doi-asserted-by":"publisher","award":["CNS-1652503"],"award-info":[{"award-number":["CNS-1652503"]}],"id":[{"id":"10.13039\/100000144","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000105","name":"Office of Advanced Cyberinfrastructure","doi-asserted-by":"publisher","award":["OAC-1934600"],"award-info":[{"award-number":["OAC-1934600"]}],"id":[{"id":"10.13039\/100000105","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2020,3,18]]},"abstract":"<jats:p>Recently, significant efforts are made to explore device-free human activity recognition techniques that utilize the information collected by existing indoor wireless infrastructures without the need for the monitored subject to carry a dedicated device. Most of the existing work, however, focuses their attention on the analysis of the signal received by a single device. In practice, there are usually multiple devices \"observing\" the same subject. Each of these devices can be regarded as an information source and provides us an unique \"view\" of the observed subject. Intuitively, if we can combine the complementary information carried by the multiple views, we will be able to improve the activity recognition accuracy. Towards this end, we propose DeepMV, a unified multi-view deep learning framework, to learn informative representations of heterogeneous device-free data. DeepMV can combine different views' information weighted by the quality of their data and extract commonness shared across different environments to improve the recognition performance. To evaluate the proposed DeepMV model, we set up a testbed using commercialized WiFi and acoustic devices. Experiment results show that DeepMV can effectively recognize activities and outperform the state-of-the-art human activity recognition methods.<\/jats:p>","DOI":"10.1145\/3380980","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T18:54:31Z","timestamp":1584557671000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["DeepMV"],"prefix":"10.1145","volume":"4","author":[{"given":"Hongfei","family":"Xue","sequence":"first","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglin","family":"Miao","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fenglong","family":"Ma","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyang","family":"Wang","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[{"name":"JD Intelligent Cities Research, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuochao","family":"Yao","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Su","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , 2016 . Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016). Mart\u00edn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218525"},{"key":"e_1_2_1_3_1","first-page":"317","article-title":"3D Tracking via Body Radio Reflections","volume":"14","author":"Adib Fadel","year":"2014","unstructured":"Fadel Adib , Zachary Kabelac , Dina Katabi , and Robert C Miller . 2014 . 3D Tracking via Body Radio Reflections .. In NSDI , Vol. 14. 317 -- 329 . Fadel Adib, Zachary Kabelac, Dina Katabi, and Robert C Miller. 2014. 3D Tracking via Body Radio Reflections.. In NSDI, Vol. 14. 317--329.","journal-title":"NSDI"},{"key":"e_1_2_1_4_1","volume-title":"Block-matching convolutional neural network for image denoising. arXiv preprint arXiv:1704.00524","author":"Ahn Byeongyong","year":"2017","unstructured":"Byeongyong Ahn and Nam Ik Cho . 2017. Block-matching convolutional neural network for image denoising. arXiv preprint arXiv:1704.00524 ( 2017 ). Byeongyong Ahn and Nam Ik Cho. 2017. Block-matching convolutional neural network for image denoising. arXiv preprint arXiv:1704.00524 (2017)."},{"key":"e_1_2_1_5_1","volume-title":"Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446","author":"Ajakan Hana","year":"2014","unstructured":"Hana Ajakan , Pascal Germain , Hugo Larochelle , Fran\u00e7ois Laviolette , and Mario Marchand . 2014. Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 ( 2014 ). Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, and Mario Marchand. 2014. Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790109"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1177"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2801073.2801078"},{"key":"e_1_2_1_9_1","first-page":"1","article-title":"Kernel independent component analysis","author":"Bach Francis R","year":"2002","unstructured":"Francis R Bach and Michael I Jordan . 2002 . Kernel independent component analysis . Journal of machine learning research 3 , Jul (2002), 1 -- 48 . Francis R Bach and Michael I Jordan. 2002. Kernel independent component analysis. Journal of machine learning research 3, Jul (2002), 1--48.","journal-title":"Journal of machine learning research 3"},{"key":"e_1_2_1_10_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073708"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2016.7457169"},{"key":"e_1_2_1_13_1","volume-title":"Seeing Through Fog Without Seeing Fog: Deep Sensor Fusion in the Absence of Labeled Training Data. arXiv preprint arXiv:1902.08913","author":"Bijelic Mario","year":"2019","unstructured":"Mario Bijelic , Fahim Mannan , Tobias Gruber , Werner Ritter , Klaus Dietmayer , and Felix Heide . 2019. Seeing Through Fog Without Seeing Fog: Deep Sensor Fusion in the Absence of Labeled Training Data. arXiv preprint arXiv:1902.08913 ( 2019 ). Mario Bijelic, Fahim Mannan, Tobias Gruber, Werner Ritter, Klaus Dietmayer, and Felix Heide. 2019. Seeing Through Fog Without Seeing Fog: Deep Sensor Fusion in the Absence of Labeled Training Data. arXiv preprint arXiv:1902.08913 (2019)."},{"key":"e_1_2_1_14_1","volume-title":"Sound Waves Gesture Recognition for Human-Computer Interaction. In International Conference on Context-Aware Systems and Applications. Springer, 41--50","author":"Binh Nguyen Dang","year":"2015","unstructured":"Nguyen Dang Binh . 2015 . Sound Waves Gesture Recognition for Human-Computer Interaction. In International Conference on Context-Aware Systems and Applications. Springer, 41--50 . Nguyen Dang Binh. 2015. Sound Waves Gesture Recognition for Human-Computer Interaction. In International Conference on Context-Aware Systems and Applications. Springer, 41--50."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860460"},{"key":"e_1_2_1_16_1","volume-title":"Proc. of the 11th Mediterranean Conf. on Control and Automation","volume":"1","author":"Bodor Robert","year":"2003","unstructured":"Robert Bodor , Bennett Jackson , and Nikolaos Papanikolopoulos . 2003 . Vision-based human tracking and activity recognition . In Proc. of the 11th Mediterranean Conf. on Control and Automation , Vol. 1 . Citeseer. Robert Bodor, Bennett Jackson, and Nikolaos Papanikolopoulos. 2003. Vision-based human tracking and activity recognition. In Proc. of the 11th Mediterranean Conf. on Control and Automation, Vol. 1. Citeseer."},{"key":"e_1_2_1_17_1","volume-title":"Distant vehicle detection using radar and vision. arXiv preprint arXiv:1901.10951","author":"Chadwick Simon","year":"2019","unstructured":"Simon Chadwick , Will Maddern , and Paul Newman . 2019. Distant vehicle detection using radar and vision. arXiv preprint arXiv:1901.10951 ( 2019 ). Simon Chadwick, Will Maddern, and Paul Newman. 2019. Distant vehicle detection using radar and vision. arXiv preprint arXiv:1901.10951 (2019)."},{"key":"e_1_2_1_18_1","unstructured":"Ning Chen Jun Zhu and Eric P Xing. 2010. Predictive subspace learning for multi-view data: a large margin approach. In Advances in neural information processing systems. 361--369.  Ning Chen Jun Zhu and Eric P Xing. 2010. Predictive subspace learning for multi-view data: a large margin approach. In Advances in neural information processing systems. 361--369."},{"key":"e_1_2_1_19_1","unstructured":"Jan K Chorowski Dzmitry Bahdanau Dmitriy Serdyuk Kyunghyun Cho and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in neural information processing systems. 577--585.  Jan K Chorowski Dzmitry Bahdanau Dmitriy Serdyuk Kyunghyun Cho and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in neural information processing systems. 577--585."},{"key":"e_1_2_1_20_1","volume-title":"Support-vector networks. Machine learning 20, 3","author":"Cortes Corinna","year":"1995","unstructured":"Corinna Cortes and Vladimir Vapnik . 1995. Support-vector networks. Machine learning 20, 3 ( 1995 ), 273--297. Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952187"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10776-018-0389-0"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2016.99"},{"key":"e_1_2_1_24_1","volume-title":"multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073","author":"Firat Orhan","year":"2016","unstructured":"Orhan Firat , Kyunghyun Cho , and Yoshua Bengio . 2016. Multi-way , multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073 ( 2016 ). Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. 2016. Multi-way, multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073 (2016)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2790044.2790046"},{"key":"e_1_2_1_26_1","volume-title":"International Conference on Machine Learning. 1180--1189","author":"Ganin Yaroslav","year":"2015","unstructured":"Yaroslav Ganin and Victor Lempitsky . 2015 . Unsupervised domain adaptation by backpropagation . In International Conference on Machine Learning. 1180--1189 . Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning. 1180--1189."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487612"},{"key":"e_1_2_1_29_1","unstructured":"Rohit Girdhar and Deva Ramanan. 2017. Attentional pooling for action recognition. In Advances in Neural Information Processing Systems. 33--44.  Rohit Girdhar and Deva Ramanan. 2017. Attentional pooling for action recognition. In Advances in Neural Information Processing Systems. 33--44."},{"key":"e_1_2_1_30_1","unstructured":"Xiaonan Guo Bo Liu Cong Shi Hongbo Liu Yingying Chen and Mooi Choo Chuah. 2017. WiFi-Enabled Smart Human Dynamics Monitoring. (2017).  Xiaonan Guo Bo Liu Cong Shi Hongbo Liu Yingying Chen and Mooi Choo Chuah. 2017. WiFi-Enabled Smart Human Dynamics Monitoring. (2017)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208331"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925861.1925870"},{"key":"e_1_2_1_33_1","volume-title":"Canonical correlation analysis: An overview with application to learning methods. Neural computation 16, 12","author":"Hardoon David R","year":"2004","unstructured":"David R Hardoon , Sandor Szedmak , and John Shawe-Taylor . 2004. Canonical correlation analysis: An overview with application to learning methods. Neural computation 16, 12 ( 2004 ), 2639--2664. David R Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004. Canonical correlation analysis: An overview with application to learning methods. Neural computation 16, 12 (2004), 2639--2664."},{"key":"e_1_2_1_34_1","volume-title":"A fast learning algorithm for deep belief nets. Neural computation 18, 7","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton , Simon Osindero , and Yee-Whye Teh . 2006. A fast learning algorithm for deep belief nets. Neural computation 18, 7 ( 2006 ), 1527--1554. Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural computation 18, 7 (2006), 1527--1554."},{"key":"e_1_2_1_35_1","volume-title":"Reducing the dimensionality of data with neural networks. science 313, 5786","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton and Ruslan R Salakhutdinov . 2006. Reducing the dimensionality of data with neural networks. science 313, 5786 ( 2006 ), 504--507. Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. science 313, 5786 (2006), 504--507."},{"key":"e_1_2_1_36_1","volume-title":"proceedings of the third international conference on","volume":"1","author":"Ho Tin Kam","year":"1995","unstructured":"Tin Kam Ho . 1995 . Random decision forests. In Document analysis and recognition, 1995 ., proceedings of the third international conference on , Vol. 1 . IEEE, 278--282. Tin Kam Ho. 1995. Random decision forests. In Document analysis and recognition, 1995., proceedings of the third international conference on, Vol. 1. IEEE, 278--282."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487860"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/28.3-4.321"},{"key":"e_1_2_1_39_1","unstructured":"Haifeng Hu Bingquan Liu Baoxun Wang Ming Liu and Xiaolong Wang. 2013. Multimodal DBN for Predicting High-Quality Answers in cQA portals.. In ACL (2). 843--847.  Haifeng Hu Bingquan Liu Baoxun Wang Ming Liu and Xiaolong Wang. 2013. Multimodal DBN for Predicting High-Quality Answers in cQA portals.. In ACL (2). 843--847."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2017.8019296"},{"key":"e_1_2_1_41_1","unstructured":"Yangqing Jia Mathieu Salzmann and Trevor Darrell. 2010. Factorized latent spaces with structured sparsity. In Advances in Neural Information Processing Systems. 982--990.  Yangqing Jia Mathieu Salzmann and Trevor Darrell. 2010. Factorized latent spaces with structured sparsity. In Advances in Neural Information Processing Systems. 982--990."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241548"},{"key":"e_1_2_1_43_1","first-page":"303","article-title":"Bringing Gesture Recognition to All Devices","volume":"14","author":"Kellogg Bryce","year":"2014","unstructured":"Bryce Kellogg , Vamsi Talla , and Shyamnath Gollakota . 2014 . Bringing Gesture Recognition to All Devices .. In NSDI , Vol. 14. 303 -- 316 . Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. 2014. Bringing Gesture Recognition to All Devices.. In NSDI, Vol. 14. 303--316.","journal-title":"NSDI"},{"key":"e_1_2_1_44_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.04.002"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971738"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906401"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130937"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2746285.2746303"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.03.007"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098088"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Ngiam Jiquan","year":"2011","unstructured":"Jiquan Ngiam , Aditya Khosla , Mingyu Kim , Juhan Nam , Honglak Lee , and Andrew Y Ng . 2011 . Multimodal deep learning . In Proceedings of the 28th international conference on machine learning (ICML-11) . 689--696. Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11). 689--696."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2671188.2749400"},{"key":"e_1_2_1_56_1","volume-title":"A survey on vision-based human action recognition. Image and vision computing 28, 6","author":"Poppe Ronald","year":"2010","unstructured":"Ronald Poppe . 2010. A survey on vision-based human action recognition. Image and vision computing 28, 6 ( 2010 ), 976--990. Ronald Poppe. 2010. A survey on vision-based human action recognition. Image and vision computing 28, 6 (2010), 976--990."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500436"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161174"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2601--2609","author":"Rastegar Sarah","year":"2016","unstructured":"Sarah Rastegar , Mahdieh Soleymani , Hamid R Rabiee , and Seyed Mohsen Shojaee . 2016 . Mdl-cw: A multimodal deep learning framework with cross weights . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2601--2609 . Sarah Rastegar, Mahdieh Soleymani, Hamid R Rabiee, and Seyed Mohsen Shojaee. 2016. Mdl-cw: A multimodal deep learning framework with cross weights. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2601--2609."},{"key":"e_1_2_1_60_1","volume-title":"End-to-end instance segmentation with recurrent attention. arXiv preprint arXiv:1605.09410","author":"Ren Mengye","year":"2017","unstructured":"Mengye Ren and Richard S Zemel . 2017. End-to-end instance segmentation with recurrent attention. arXiv preprint arXiv:1605.09410 ( 2017 ). Mengye Ren and Richard S Zemel. 2017. End-to-end instance segmentation with recurrent attention. arXiv preprint arXiv:1605.09410 (2017)."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971736"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2459236.2459254"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2536853.2536873"},{"key":"e_1_2_1_64_1","doi-asserted-by":"crossref","unstructured":"Carina Silberer and Mirella Lapata. 2014. Learning Grounded Meaning Representations with Autoencoders.. In ACL (1). 721--732.  Carina Silberer and Mirella Lapata. 2014. Learning Grounded Meaning Representations with Autoencoders.. In ACL (1). 721--732.","DOI":"10.3115\/v1\/P14-1068"},{"key":"e_1_2_1_65_1","unstructured":"Kihyuk Sohn Wenling Shang and Honglak Lee. 2014. Improved multimodal deep learning with variation of information. In Advances in Neural Information Processing Systems. 2141--2149.  Kihyuk Sohn Wenling Shang and Honglak Lee. 2014. Improved multimodal deep learning with variation of information. In Advances in Neural Information Processing Systems. 2141--2149."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697059"},{"key":"e_1_2_1_68_1","volume-title":"LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108","author":"Tan Ming","year":"2015","unstructured":"Ming Tan , Cicero dos Santos , Bing Xiang , and Bowen Zhou . 2015. LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108 ( 2015 ). Ming Tan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. 2015. LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108 (2015)."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2942358.2942393"},{"key":"e_1_2_1_70_1","volume-title":"Correction by projection: Denoising images with generative adversarial networks. arXiv preprint arXiv:1803.04477","author":"Tripathi Subarna","year":"2018","unstructured":"Subarna Tripathi , Zachary C Lipton , and Truong Q Nguyen . 2018. Correction by projection: Denoising images with generative adversarial networks. arXiv preprint arXiv:1803.04477 ( 2018 ). Subarna Tripathi, Zachary C Lipton, and Truong Q Nguyen. 2018. Correction by projection: Denoising images with generative adversarial networks. arXiv preprint arXiv:1803.04477 (2018)."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"},{"key":"e_1_2_1_72_1","first-page":"4","article-title":"Adversarial discriminative domain adaptation","volume":"1","author":"Tzeng Eric","year":"2017","unstructured":"Eric Tzeng , Judy Hoffman , Kate Saenko , and Trevor Darrell . 2017 . Adversarial discriminative domain adaptation . In Computer Vision and Pattern Recognition (CVPR) , Vol. 1. 4 . Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017. Adversarial discriminative domain adaptation. In Computer Vision and Pattern Recognition (CVPR), Vol. 1. 4.","journal-title":"Computer Vision and Pattern Recognition (CVPR)"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_2_1_74_1","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","author":"Vincent Pascal","year":"2010","unstructured":"Pascal Vincent , Hugo Larochelle , Isabelle Lajoie , Yoshua Bengio , and Pierre-Antoine Manzagol . 2010 . Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion . Journal of machine learning research 11 , Dec (2010), 3371 -- 3408 . Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research 11, Dec (2010), 3371--3408.","journal-title":"Journal of machine learning research 11"},{"key":"e_1_2_1_75_1","volume-title":"We can hear you with wi-fi! IEEE Transactions on Mobile Computing 15, 11","author":"Wang Guanhua","year":"2016","unstructured":"Guanhua Wang , Yongpan Zou , Zimu Zhou , Kaishun Wu , and Lionel M Ni. 2016. We can hear you with wi-fi! IEEE Transactions on Mobile Computing 15, 11 ( 2016 ), 2907--2920. Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M Ni. 2016. We can hear you with wi-fi! IEEE Transactions on Mobile Computing 15, 11 (2016), 2907--2920."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971744"},{"key":"e_1_2_1_77_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML-15)","author":"Wang Weiran","year":"2015","unstructured":"Weiran Wang , Raman Arora , Karen Livescu , and Jeff Bilmes . 2015 . On deep multi-view representation learning . In Proceedings of the 32nd International Conference on Machine Learning (ICML-15) . 1083--1092. Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. 2015. On deep multi-view representation learning. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15). 1083--1092."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971670"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790093"},{"key":"e_1_2_1_80_1","volume-title":"Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 82--94","author":"Wang Wei","year":"2016","unstructured":"Wei Wang , Alex X Liu , and Ke Sun . 2016 . Device-free gesture tracking using acoustic signals . In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 82--94 . Wei Wang, Alex X Liu, and Ke Sun. 2016. Device-free gesture tracking using acoustic signals. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 82--94."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639143"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2557792"},{"key":"e_1_2_1_83_1","volume-title":"Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1--3","author":"Wold Svante","year":"1987","unstructured":"Svante Wold , Kim Esbensen , and Paul Geladi . 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1--3 ( 1987 ), 37--52. Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2, 1--3 (1987), 37--52."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700143"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"e_1_2_1_86_1","volume-title":"Mining associated text and images with dual-wing harmoniums. arXiv preprint arXiv:1207.1423","author":"Xing Eric P","year":"2012","unstructured":"Eric P Xing , Rong Yan , and Alexander G Hauptmann . 2012. Mining associated text and images with dual-wing harmoniums. arXiv preprint arXiv:1207.1423 ( 2012 ). Eric P Xing, Rong Yan, and Alexander G Hauptmann. 2012. Mining associated text and images with dual-wing harmoniums. arXiv preprint arXiv:1207.1423 (2012)."},{"key":"e_1_2_1_87_1","volume-title":"International Conference on Machine Learning. 2048--2057","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu , Jimmy Ba , Ryan Kiros , Kyunghyun Cho , Aaron Courville , Ruslan Salakhudinov , Rich Zemel , and Yoshua Bengio . 2015 . Show, attend and tell: Neural image caption generation with visual attention . In International Conference on Machine Learning. 2048--2057 . Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning. 2048--2057."},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1145\/3323679.3326513"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3212725.3212729"},{"key":"e_1_2_1_91_1","volume-title":"SADeepSense: Self-Attention Deep Learning Framework for Heterogeneous On-Device Sensors in Internet of Things Applications","author":"Yao Shuochao","year":"2019","unstructured":"Shuochao Yao , Yiran Zhao , Huajie Shao , Dongxin Liu , Shengzhong Liu , Yifan Hao , Ailing Piao , Shaohan Hu , Su Lu , and Tarek F Abdelzaher . 2019. SADeepSense: Self-Attention Deep Learning Framework for Heterogeneous On-Device Sensors in Internet of Things Applications . In IEEE INFOCOM 2019 -IEEE Conference on Computer Communications. IEEE , 1243--1251. Shuochao Yao, Yiran Zhao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Yifan Hao, Ailing Piao, Shaohan Hu, Su Lu, and Tarek F Abdelzaher. 2019. SADeepSense: Self-Attention Deep Learning Framework for Heterogeneous On-Device Sensors in Internet of Things Applications. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 1243--1251."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/3107411.3107419"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2018.8333405"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00087"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359158"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/DCOSS.2016.30"},{"key":"e_1_2_1_97_1","volume-title":"International Conference on Machine Learning. 4100--4109","author":"Zhao Mingmin","year":"2017","unstructured":"Mingmin Zhao , Shichao Yue , Dina Katabi , Tommi S Jaakkola , and Matt T Bianchi . 2017 . Learning sleep stages from radio signals: a conditional adversarial architecture . In International Conference on Machine Learning. 4100--4109 . Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S Jaakkola, and Matt T Bianchi. 2017. Learning sleep stages from radio signals: a conditional adversarial architecture. In International Conference on Machine Learning. 4100--4109."},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2017.2762428"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3380980","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3380980","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3380980","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:32:46Z","timestamp":1750199566000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3380980"}},"subtitle":["Multi-View Deep Learning for Device-Free Human Activity Recognition"],"short-title":[],"issued":{"date-parts":[[2020,3,18]]},"references-count":98,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,3,18]]}},"alternative-id":["10.1145\/3380980"],"URL":"https:\/\/doi.org\/10.1145\/3380980","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,18]]},"assertion":[{"value":"2020-03-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}