乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:57:21Z","timestamp":1725911841157},"reference-count":119,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"Recently, IMUTube introduced a paradigm change for bootstrapping human activity recognition (HAR) systems for wearables. The key idea is to utilize videos of activities to support training activity recognizers based on inertial measurement units (IMUs). This system retrieves video from public repositories and subsequently generates virtual IMU data from this. The ultimate vision for such a system is to make large amounts of weakly labeled videos accessible for model training in HAR and, as such, to overcome one of the most pressing issues in the field: the lack of significant amounts of labeled sample data. In this paper we present the first in-detail exploration of IMUTube in a realistic assessment scenario: the analysis of free-weight gym exercises. We make significant progress towards a flexible, fully-functional IMUTube system by extending it such that it can handle a range of artifacts that are common in unrestricted online videos, including various forms of video noise, non-human poses, body part occlusions, and extreme camera and human motion. By overcoming these real-world challenges, we are able to generate high-quality virtual IMU data, which allows us to employ IMUTube for practical analysis tasks. We show that HAR systems trained by incorporating virtual sensor data generated by IMUTube significantly outperform baseline models trained only with real IMU data. In doing so we demonstrate the practical utility of IMUTube and the progress made towards the final vision of the new bootstrapping paradigm.<\/jats:p>","DOI":"10.1145\/3478096","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T22:48:23Z","timestamp":1631659703000},"page":"1-32","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Approaching the Real-World"],"prefix":"10.1145","volume":"5","author":[{"given":"Hyeokhyen","family":"Kwon","sequence":"first","affiliation":[{"name":"School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Bingyao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computing, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Gregory D.","family":"Abowd","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA"}]},{"given":"Thomas","family":"Pl\u00f6tz","sequence":"additional","affiliation":[{"name":"School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2036165"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0312-3"},{"volume-title":"IEEE International Conference on Image Processing (ICIP). 3464--3468","author":"Bewley A.","key":"e_1_2_1_3_1","unstructured":"A. Bewley , Z. Ge , L. Ott , F. Ramos , and B. Upcroft . 2016. Simple online and realtime tracking . In IEEE International Conference on Image Processing (ICIP). 3464--3468 . A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft. 2016. Simple online and realtime tracking. In IEEE International Conference on Image Processing (ICIP). 3464--3468."},{"key":"e_1_2_1_4_1","first-page":"1","article-title":"DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras","volume":"6","author":"Bogdan O.","year":"2018","unstructured":"O. Bogdan , V. Eckstein , F. Rameau , and J. Bazin . 2018 . DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras . In Proceedings of the ACM SIGGRAPH European Conference on Visual Media Production. ACM , 6 : 1 - 6 :10. O. Bogdan, V. Eckstein, F. Rameau, and J. Bazin. 2018. DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras. In Proceedings of the ACM SIGGRAPH European Conference on Visual Media Production. ACM, 6:1-6:10.","journal-title":"Proceedings of the ACM SIGGRAPH European Conference on Visual Media Production. ACM"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499621"},{"key":"e_1_2_1_6_1","unstructured":"Z. Cao G. Hidalgo Martinez T. Simon S. Wei and Y. A. Sheikh. 2019. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (2019). Z. Cao G. Hidalgo Martinez T. Simon S. Wei and Y. A. Sheikh. 2019. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)."},{"volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 4733--4742","author":"Carreira J.","key":"e_1_2_1_7_1","unstructured":"J. Carreira , P. Agrawal , K. Fragkiadaki , and J. Malik . 2016. Human pose estimation with iterative error feedback . In Proceedings of the IEEE conference on computer vision and pattern recognition. 4733--4742 . J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik. 2016. Human pose estimation with iterative error feedback. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4733--4742."},{"volume-title":"Christina Kelley, Kaya de Barbaro, Gregory D. Abowd, and Lauren Wilcox.","year":"2018","author":"Chan Larry","key":"e_1_2_1_8_1","unstructured":"Larry Chan , Vedant Das Swain , Christina Kelley, Kaya de Barbaro, Gregory D. Abowd, and Lauren Wilcox. 2018 . Students' Experiences with Ecological Momentary Assessment Tools to Report on Emotional Well-Being. IMWUT 2, 1, Article 3 (March 2018), 20 pages. Larry Chan, Vedant Das Swain, Christina Kelley, Kaya de Barbaro, Gregory D. Abowd, and Lauren Wilcox. 2018. Students' Experiences with Ecological Momentary Assessment Tools to Report on Emotional Well-Being. IMWUT 2, 1, Article 3 (March 2018), 20 pages."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2012.12.014"},{"key":"e_1_2_1_10_1","unstructured":"M. Christ A. W. Kempa-Liehr and M. Feindt. 2016. Distributed and parallel time series feature extraction for industrial big data applications. https:\/\/tsfresh.readthedocs.io. arXiv preprint arXiv:1610.07717 (2016). M. Christ A. W. Kempa-Liehr and M. Feindt. 2016. Distributed and parallel time series feature extraction for industrial big data applications. https:\/\/tsfresh.readthedocs.io. arXiv preprint arXiv:1610.07717 (2016)."},{"volume-title":"Blender - a 3D modelling and rendering package","author":"Community Blender Online","key":"e_1_2_1_11_1","unstructured":"Blender Online Community . 2018. Blender - a 3D modelling and rendering package . Blender Foundation, Stichting Blender Foundation, Amsterdam . http:\/\/www.blender.org Blender Online Community. 2018. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam. http:\/\/www.blender.org"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1080\/00031305.1981.10479327","article-title":"Rank transformations as a bridge between parametric and nonparametric statistics","volume":"35","author":"Conover W.","year":"1981","unstructured":"W. Conover and R. Iman . 1981 . Rank transformations as a bridge between parametric and nonparametric statistics . The American Statistician 35 , 3 (1981), 124 -- 129 . W. Conover and R. Iman. 1981. Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician 35, 3 (1981), 124--129.","journal-title":"The American Statistician"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2003.1203750"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_32"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299025"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.316"},{"volume-title":"RMPE: Regional Multi-person Pose Estimation. In ICCV.","year":"2017","author":"Fang Hao-Shu","key":"e_1_2_1_17_1","unstructured":"Hao-Shu Fang , Shuqin Xie , Yu-Wing Tai , and Cewu Lu . 2017 . RMPE: Regional Multi-person Pose Estimation. In ICCV. Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, and Cewu Lu. 2017. RMPE: Regional Multi-person Pose Estimation. In ICCV."},{"key":"e_1_2_1_18_1","unstructured":"H. Fawaz G. Forestier J. Weber L. Idoumghar and P. Muller. 2018. Data augmentation using synthetic data for time series classification with deep residual networks. arXiv preprint arXiv:1808.02455 (2018). H. Fawaz G. Forestier J. Weber L. Idoumghar and P. Muller. 2018. Data augmentation using synthetic data for time series classification with deep residual networks. arXiv preprint arXiv:1808.02455 (2018)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-010-0293-9"},{"volume-title":"Kamalveer Kaur Garewal, and Paul Lukowicz","year":"2020","author":"Rey Vitor Fortes","key":"e_1_2_1_20_1","unstructured":"Vitor Fortes Rey , Kamalveer Kaur Garewal, and Paul Lukowicz . 2020 . Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos. arXiv e-prints (2020), arXiv-2011. Vitor Fortes Rey, Kamalveer Kaur Garewal, and Paul Lukowicz. 2020. Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos. arXiv e-prints (2020), arXiv-2011."},{"volume-title":"The KITTI Vision Benchmark Suite. In Conference on Computer Vision and Pattern Recognition (CVPR).","year":"2012","author":"Geiger Andreas","key":"e_1_2_1_21_1","unstructured":"Andreas Geiger , Philip Lenz , and Raquel Urtasun . 2012 . Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Conference on Computer Vision and Pattern Recognition (CVPR). Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298641"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00763"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_47"},{"key":"e_1_2_1_25_1","unstructured":"I. Goodfellow J. Pouget-Abadie M. Mirza B. Xu D. Warde-Farley S. Ozair A. Courville and Y. Bengio. 2014. Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014). I. Goodfellow J. Pouget-Abadie M. Mirza B. Xu D. Warde-Farley S. Ozair A. Courville and Y. Bengio. 2014. Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)."},{"volume-title":"Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras. In IEEE International Conference on Computer Vision (ICCV). IEEE.","author":"Gordon A.","key":"e_1_2_1_26_1","unstructured":"A. Gordon , H. Li , R. Jonschkowski , and A. Angelova . 2019 . Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras. In IEEE International Conference on Computer Vision (ICCV). IEEE. A. Gordon, H. Li, R. Jonschkowski, and A. Angelova. 2019. Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras. In IEEE International Conference on Computer Vision (ICCV). IEEE."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2019.00020"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00039"},{"key":"e_1_2_1_29_1","unstructured":"John K Haas. 2014. A history of the unity game engine. (2014). John K Haas. 2014. A history of the unity game engine. (2014)."},{"volume-title":"Proceedings of the ACM International Symposium on Wearable Computers. 65--68","author":"Hammerla N.","key":"e_1_2_1_30_1","unstructured":"N. Hammerla , R. Kirkham , P. Andras , and T. Ploetz . 2013. On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution . In Proceedings of the ACM International Symposium on Wearable Computers. 65--68 . N. Hammerla, R. Kirkham, P. Andras, and T. Ploetz. 2013. On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution. In Proceedings of the ACM International Symposium on Wearable Computers. 65--68."},{"volume-title":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 368--371","author":"Haradal S.","key":"e_1_2_1_31_1","unstructured":"S. Haradal , H. Hayashi , and S. Uchida . 2018. Biosignal data augmentation based on generative adversarial networks . In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 368--371 . S. Haradal, H. Hayashi, and S. Uchida. 2018. Biosignal data augmentation based on generative adversarial networks. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 368--371."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"R. I. Hartley and A. Zisserman. 2004. Multiple View Geometry in Computer Vision (second ed.). Cambridge University Press ISBN: 0521540518. R. I. Hartley and A. Zisserman. 2004. Multiple View Geometry in Computer Vision (second ed.). Cambridge University Press ISBN: 0521540518.","DOI":"10.1017\/CBO9780511811685"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6728"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"volume-title":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778","author":"He K.","key":"e_1_2_1_37_1","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun . 2016. Deep residual learning for image recognition . In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778 . K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778."},{"volume-title":"The devil is in the tails: Fine-grained classification in the wild. arXiv preprint arXiv:1709.01450 2","year":"2017","author":"Horn GV","key":"e_1_2_1_38_1","unstructured":"GV Horn and Pietro Perona . 2017. The devil is in the tails: Fine-grained classification in the wild. arXiv preprint arXiv:1709.01450 2 ( 2017 ). GV Horn and Pietro Perona. 2017. The devil is in the tails: Fine-grained classification in the wild. arXiv preprint arXiv:1709.01450 2 (2017)."},{"volume-title":"Reliable Monocular Ego-Motion Estimation System in Rainy Urban Environments. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC). 1290--1297","year":"2019","author":"Huang H.","key":"e_1_2_1_39_1","unstructured":"H. Huang , Y. Sun , and M. Liu . 2019 . Reliable Monocular Ego-Motion Estimation System in Rainy Urban Environments. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC). 1290--1297 . https:\/\/doi.org\/10.1109\/ITSC. 2019 .8916977 H. Huang, Y. Sun, and M. Liu. 2019. Reliable Monocular Ego-Motion Estimation System in Rainy Urban Environments. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC). 1290--1297. https:\/\/doi.org\/10.1109\/ITSC.2019.8916977"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00590"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.435"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.179"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_42"},{"volume-title":"European Conference on Computer Vision. Springer, 3--10","year":"2016","author":"Jason J Yu","key":"e_1_2_1_44_1","unstructured":"J Yu Jason , Adam W Harley , and Konstantinos G Derpanis . 2016 . Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness . In European Conference on Computer Vision. Springer, 3--10 . J Yu Jason, Adam W Harley, and Konstantinos G Derpanis. 2016. Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness. In European Conference on Computer Vision. Springer, 3--10."},{"volume-title":"IEEE International Conference on Computer Vision (ICCV). IEEE.","year":"2011","author":"Joel I.","key":"e_1_2_1_45_1","unstructured":"I. Joel , A.and Stergios. 2011 . A Direct Least-Squares (DLS) method for PnP . In IEEE International Conference on Computer Vision (ICCV). IEEE. I. Joel, A.and Stergios. 2011. A Direct Least-Squares (DLS) method for PnP. In IEEE International Conference on Computer Vision (ICCV). IEEE."},{"volume-title":"Towards Machine Learning with Zero Real-World Data. In The ACM Workshop on Wearable Systems and Applications. 41--46","author":"Kang C.","key":"e_1_2_1_46_1","unstructured":"C. Kang , H. Jung , and Y. Lee . 2019 . Towards Machine Learning with Zero Real-World Data. In The ACM Workshop on Wearable Systems and Applications. 41--46 . C. Kang, H. Jung, and Y. Lee. 2019. Towards Machine Learning with Zero Real-World Data. In The ACM Workshop on Wearable Systems and Applications. 41--46."},{"volume-title":"Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 537--546","author":"Koskim\u00e4ki H.","key":"e_1_2_1_47_1","unstructured":"H. Koskim\u00e4ki , P. Siirtola , and J. R\u00f6ning . 2017. MyoGym: Introducing an Open Gym Data Set for Activity Recognition Collected Using Myo Armband . In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 537--546 . H. Koskim\u00e4ki, P. Siirtola, and J. R\u00f6ning. 2017. MyoGym: Introducing an Open Gym Data Set for Activity Recognition Collected Using Myo Armband. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 537--546."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2014.7008685"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341163.3347744"},{"volume-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 3, Article 87 (Sept.","year":"2020","author":"Kwon H.","key":"e_1_2_1_50_1","unstructured":"H. Kwon , C. Tong , H. Haresamudram , Y. Gao , G. D. Abowd , N. D. Lane , and T. Pl\u00f6tz . 2020. IMUTube: Automatic Extraction of Virtual on-Body Accelerometry from Video for Human Activity Recognition . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 3, Article 87 (Sept. 2020 ), 29 pages. H. Kwon, C. Tong, H. Haresamudram, Y. Gao, G. D. Abowd, N. D. Lane, and T. Pl\u00f6tz. 2020. IMUTube: Automatic Extraction of Virtual on-Body Accelerometry from Video for Human Activity Recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 3, Article 87 (Sept. 2020), 29 pages."},{"key":"e_1_2_1_51_1","unstructured":"Carnegie Mellon Graphics Lab. 2008. Carnegie Mellon Motion Capture Database. http:\/\/mocap.cs.cmu.edu\/ Carnegie Mellon Graphics Lab. 2008. Carnegie Mellon Motion Capture Database. http:\/\/mocap.cs.cmu.edu\/"},{"volume-title":"A survey on human activity recognition using wearable sensors","year":"2012","author":"Lara O. D.","key":"e_1_2_1_52_1","unstructured":"O. D. Lara and M. A Labrador . 2012. A survey on human activity recognition using wearable sensors . IEEE communications surveys & tutorials 15, 3 ( 2012 ), 1192--1209. O. D. Lara and M. A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192--1209."},{"volume-title":"ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data.","author":"Guennec A. Le","key":"e_1_2_1_53_1","unstructured":"A. Le Guennec , S. Malinowski , and R. Tavenard . 2016. Data Augmentation for Time Series Classification using Convolutional Neural Networks . In ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. A. Le Guennec, S. Malinowski, and R. Tavenard. 2016. Data Augmentation for Time Series Classification using Convolutional Neural Networks. In ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data."},{"key":"e_1_2_1_54_1","doi-asserted-by":"crossref","unstructured":"X. Li J. Luo and R. Younes. 2020. ActivityGAN: Generative Adversarial Networks for Data Augmentation in Sensor-Based Human Activity Recognition (UbiComp-ISWC '20). ACM 249--254. X. Li J. Luo and R. Younes. 2020. ActivityGAN: Generative Adversarial Networks for Data Augmentation in Sensor-Based Human Activity Recognition (UbiComp-ISWC '20). ACM 249--254.","DOI":"10.1145\/3410530.3414367"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.472"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328927"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00652"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018770"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00470"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.378"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"volume-title":"IEEE International Conference on Computer Vision (ICCV). 5442--5451","author":"Mahmood N.","key":"e_1_2_1_62_1","unstructured":"N. Mahmood , N. Ghorbani , N. Troje , G. Pons-Moll , and M. Black . 2019. AMASS: Archive of motion capture as surface shapes . In IEEE International Conference on Computer Vision (ICCV). 5442--5451 . N. Mahmood, N. Ghorbani, N. Troje, G. Pons-Moll, and M. Black. 2019. AMASS: Archive of motion capture as surface shapes. In IEEE International Conference on Computer Vision (ICCV). 5442--5451."},{"volume-title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. arXiv preprint arXiv:1603.09056","year":"2016","author":"Mao Xiao-Jiao","key":"e_1_2_1_63_1","unstructured":"Xiao-Jiao Mao , Chunhua Shen , and Yu-Bin Yang . 2016. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. arXiv preprint arXiv:1603.09056 ( 2016 ). Xiao-Jiao Mao, Chunhua Shen, and Yu-Bin Yang. 2016. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. arXiv preprint arXiv:1603.09056 (2016)."},{"volume-title":"IEEE International Conference on Computer Vision (ICCV). 2640--2649","author":"Martinez J.","key":"e_1_2_1_64_1","unstructured":"J. Martinez , R. Hossain , J. Romero , and J. Little . 2017. A simple yet effective baseline for 3d human pose estimation . In IEEE International Conference on Computer Vision (ICCV). 2640--2649 . J. Martinez, R. Hossain, J. Romero, and J. Little. 2017. A simple yet effective baseline for 3d human pose estimation. In IEEE International Conference on Computer Vision (ICCV). 2640--2649."},{"volume-title":"IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 200--211","author":"Mathur A.","key":"e_1_2_1_65_1","unstructured":"A. Mathur , T. Zhang , S. Bhattacharya , P. Velickovic , L. Joffe , N. Lane , F. Kawsar , and P. Li\u00f3 . 2018. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices . In IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 200--211 . A. Mathur, T. Zhang, S. Bhattacharya, P. Velickovic, L.Joffe, N. Lane, F. Kawsar, and P. Li\u00f3. 2018. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. In IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 200--211."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.438"},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","unstructured":"D. Mehta O. Sotnychenko F. Mueller W. Xu M. Elgharib P. Fua H. Seidel H. Rhodin G. Pons-Moll and C. Theobalt. 2020. XNect: Real-Time Multi-Person 3D Motion Capture with a Single RGB Camera. ToG. 39 4 Article 82 (July 2020) 17 pages. D. Mehta O. Sotnychenko F. Mueller W. Xu M. Elgharib P. Fua H. Seidel H. Rhodin G. Pons-Moll and C. Theobalt. 2020. XNect: Real-Time Multi-Person 3D Motion Capture with a Single RGB Camera. ToG. 39 4 Article 82 (July 2020) 17 pages.","DOI":"10.1145\/3386569.3392410"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557116"},{"volume-title":"Ismail Ben Ayed, and Marco Pedersoli","year":"2019","author":"Mounsaveng Saypraseuth","key":"e_1_2_1_70_1","unstructured":"Saypraseuth Mounsaveng , David Vazquez , Ismail Ben Ayed, and Marco Pedersoli . 2019 . Adversarial learning of general transformations for data augmentation. arXiv preprint arXiv:1909.09801 (2019). Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed, and Marco Pedersoli. 2019. Adversarial learning of general transformations for data augmentation. arXiv preprint arXiv:1909.09801 (2019)."},{"volume-title":"European conference on computer vision. Springer, 483--499","author":"Newell A.","key":"e_1_2_1_71_1","unstructured":"A. Newell , K. Yang , and J. Deng . 2016. Stacked hourglass networks for human pose estimation . In European conference on computer vision. Springer, 483--499 . A. Newell, K. Yang, and J. Deng. 2016. Stacked hourglass networks for human pose estimation. In European conference on computer vision. Springer, 483--499."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_31"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"volume-title":"Cascaded Deep Video Deblurring Using Temporal Sharpness Prior. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","year":"2020","author":"Pan Jinshan","key":"e_1_2_1_74_1","unstructured":"Jinshan Pan , Haoran Bai , and Jinhui Tang . 2020 . Cascaded Deep Video Deblurring Using Temporal Sharpness Prior. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Jinshan Pan, Haoran Bai, and Jinhui Tang. 2020. Cascaded Deep Video Deblurring Using Temporal Sharpness Prior. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_17"},{"volume-title":"Colored Point Cloud Registration Revisited. In IEEE International Conference on Computer Vision (ICCV). 143--152","author":"Park J.","key":"e_1_2_1_76_1","unstructured":"J. Park , Q. Zhou , and V. Koltun . 2017 . Colored Point Cloud Registration Revisited. In IEEE International Conference on Computer Vision (ICCV). 143--152 . J. Park, Q. Zhou, and V. Koltun. 2017. Colored Point Cloud Registration Revisited. In IEEE International Conference on Computer Vision (ICCV). 143--152."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"volume-title":"The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7753--7762","author":"Pavllo D.","key":"e_1_2_1_78_1","unstructured":"D. Pavllo , C. Feichtenhofer , D. Grangier , and M. Auli . 2019. 3D human pose estimation in video with temporal convolutions and semi-supervised training . In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7753--7762 . D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli. 2019. 3D human pose estimation in video with temporal convolutions and semi-supervised training. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7753--7762."},{"volume-title":"MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model","year":"2021","author":"Pei L.","key":"e_1_2_1_79_1","unstructured":"L. Pei , S. Xia , L. Chu , F. Xiao , Q. Wu , W. Yu , and R. Qiu . 2021 . MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model . IEEE Internet of Things Journal ( 2021). L. Pei, S. Xia, L. Chu, F. Xiao, Q. Wu, W. Yu, and R. Qiu. 2021. MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model. IEEE Internet of Things Journal (2021)."},{"volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 4929--4937","author":"Pishchulin L.","key":"e_1_2_1_80_1","unstructured":"L. Pishchulin , E. Insafutdinov , S. Tang , B. Andres , M. Andriluka , P. Gehler , and B. Schiele . 2016. Deepcut: Joint subset partition and labeling for multi person pose estimation . In Proceedings of the IEEE conference on computer vision and pattern recognition. 4929--4937 . L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. Gehler, and B. Schiele. 2016. Deepcut: Joint subset partition and labeling for multi person pose estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4929--4937."},{"volume-title":"YOLOv3: An Incremental Improvement. arXiv","year":"2018","author":"Redmon Joseph","key":"e_1_2_1_81_1","unstructured":"Joseph Redmon and Ali Farhadi . 2018. YOLOv3: An Incremental Improvement. arXiv ( 2018 ). Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. arXiv (2018)."},{"volume-title":"Proceedings of the ACM International Symposium on Wearable Computers. IEEE, 108--109","author":"Reiss A.","key":"e_1_2_1_82_1","unstructured":"A. Reiss and D. Stricker . 2012. Introducing a new benchmarked dataset for activity monitoring . In Proceedings of the ACM International Symposium on Wearable Computers. IEEE, 108--109 . A. Reiss and D. Stricker. 2012. Introducing a new benchmarked dataset for activity monitoring. In Proceedings of the ACM International Symposium on Wearable Computers. IEEE, 108--109."},{"volume-title":"Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. 699--708","author":"Rey V.","key":"e_1_2_1_83_1","unstructured":"V. Rey , P. Hevesi , O. Kovalenko , and P. Lukowicz . 2019. Let there be IMU data: generating training data for wearable, motion sensor based activity recognition from monocular RGB videos . In Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. 699--708 . V. Rey, P. Hevesi, O. Kovalenko, and P. Lukowicz. 2019. Let there be IMU data: generating training data for wearable, motion sensor based activity recognition from monocular RGB videos. In Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. 699--708."},{"volume-title":"Rough and Aaron Quigley","year":"2020","author":"Daniel","key":"e_1_2_1_84_1","unstructured":"Daniel J. Rough and Aaron Quigley . 2020 . End-User Development of Experience Sampling Smartphone Apps-Recommendations and Requirements. IMWUT 4, 2, Article 56 (June 2020), 19 pages. Daniel J. Rough and Aaron Quigley. 2020. End-User Development of Experience Sampling Smartphone Apps-Recommendations and Requirements. IMWUT 4, 2, Article 56 (June 2020), 19 pages."},{"volume-title":"Proceedings of the International Conference on Ubiquitous Computing. ACM, 589--599","author":"Scholl P. M.","key":"e_1_2_1_85_1","unstructured":"P. M. Scholl , M. Wille , and K. Van Laerhoven . 2015. Wearables in the wet lab: a laboratory system for capturing and guiding experiments . In Proceedings of the International Conference on Ubiquitous Computing. ACM, 589--599 . P. M. Scholl, M. Wille, and K. Van Laerhoven. 2015. Wearables in the wet lab: a laboratory system for capturing and guiding experiments. In Proceedings of the International Conference on Ubiquitous Computing. ACM, 589--599."},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.55.5443"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1145\/325334.325242"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_1_89_1","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014). K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)."},{"key":"e_1_2_1_90_1","doi-asserted-by":"crossref","unstructured":"D. Str\u00f6mb\u00e4ck S. Huang and V. Radu. 2020. MM-Fit: Multimodal Deep Learning for Automatic Exercise Logging across Sensing Devices. IMWUT 4 4 Article 168 (Dec. 2020) 22 pages. D. Str\u00f6mb\u00e4ck S. Huang and V. Radu. 2020. MM-Fit: Multimodal Deep Learning for Automatic Exercise Logging across Sensing Devices. IMWUT 4 4 Article 168 (Dec. 2020) 22 pages.","DOI":"10.1145\/3432701"},{"volume-title":"Proc. of the International Conference on Intelligent Robot Systems (IROS).","author":"Sturm J.","key":"e_1_2_1_91_1","unstructured":"J. Sturm , N. Engelhard , F. Endres , W. Burgard , and D. Cremers . 2012. A Benchmark for the Evaluation of RGB-D SLAM Systems . In Proc. of the International Conference on Intelligent Robot Systems (IROS). J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers. 2012. A Benchmark for the Evaluation of RGB-D SLAM Systems. In Proc. of the International Conference on Intelligent Robot Systems (IROS)."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9040746"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00931"},{"volume-title":"IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 1--9.","author":"Sztyler T.","key":"e_1_2_1_94_1","unstructured":"T. Sztyler and H. Stuckenschmidt . 2016. On-body localization of wearable devices: An investigation of position-aware activity recognition . In IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 1--9. T. Sztyler and H. Stuckenschmidt. 2016. On-body localization of wearable devices: An investigation of position-aware activity recognition. In IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 1--9."},{"volume-title":"Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1444--1448","author":"Takeda S.","key":"e_1_2_1_95_1","unstructured":"S. Takeda , T. Okita , P. Lago , and S. Inoue . 2018. A multi-sensor setting activity recognition simulation tool . In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1444--1448 . S. Takeda, T. Okita, P. Lago, and S. Inoue. 2018. A multi-sensor setting activity recognition simulation tool. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1444--1448."},{"volume-title":"Pose2instance: Harnessing keypoints for person instance segmentation. arXiv preprint arXiv:1704.01152","year":"2017","author":"Tripathi Subarna","key":"e_1_2_1_96_1","unstructured":"Subarna Tripathi , Maxwell Collins , Matthew Brown , and Serge Belongie . 2017. Pose2instance: Harnessing keypoints for person instance segmentation. arXiv preprint arXiv:1704.01152 ( 2017 ). Subarna Tripathi, Maxwell Collins, Matthew Brown, and Serge Belongie. 2017. Pose2instance: Harnessing keypoints for person instance segmentation. arXiv preprint arXiv:1704.01152 (2017)."},{"volume-title":"Proceedings of the ACM International Conference on Multimodal Interaction. 216--220","author":"Um T.","key":"e_1_2_1_97_1","unstructured":"T. Um , F. Pfister , D. Pichler , S. Endo , M. Lang , S. Hirche , U. Fietzek , and D. Kuli\u0107 . 2017. Data augmentation of wearable sensor data for parkinson's disease monitoring using convolutional neural networks . In Proceedings of the ACM International Conference on Multimodal Interaction. 216--220 . T. Um, F. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kuli\u0107. 2017. Data augmentation of wearable sensor data for parkinson's disease monitoring using convolutional neural networks. In Proceedings of the ACM International Conference on Multimodal Interaction. 216--220."},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206051"},{"volume-title":"Article 107 (Sept.","year":"2017","author":"van Berkel Niels","key":"e_1_2_1_99_1","unstructured":"Niels van Berkel , Jorge Goncalves , Simo Hosio , and Vassilis Kostakos . 2017. Gamification of Mobile Experience Sampling Improves Data Quality and Quantity. IMWUT 1, 3 , Article 107 (Sept. 2017 ), 21 pages. Niels van Berkel, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2017. Gamification of Mobile Experience Sampling Improves Data Quality and Quantity. IMWUT 1, 3, Article 107 (Sept. 2017), 21 pages."},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_27"},{"key":"e_1_2_1_101_1","unstructured":"A. Vaswani N. Shazeer N. Parmar J. Uszkoreit L. Jones A. N. Gomez L. Kaiser and I. Polosukhin. 2017. Attention is all you need. arXiv:1706.03762 (2017). A. Vaswani N. Shazeer N. Parmar J. Uszkoreit L. Jones A. N. Gomez L. Kaiser and I. Polosukhin. 2017. Attention is all you need. arXiv:1706.03762 (2017)."},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00513"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1927.10502953"},{"volume-title":"Spatially transformed adversarial examples. arXiv preprint arXiv:1801.02612","year":"2018","author":"Xiao Chaowei","key":"e_1_2_1_105_1","unstructured":"Chaowei Xiao , Jun-Yan Zhu , Bo Li , Warren He , Mingyan Liu , and Dawn Song . 2018. Spatially transformed adversarial examples. arXiv preprint arXiv:1801.02612 ( 2018 ). Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song. 2018. Spatially transformed adversarial examples. arXiv preprint arXiv:1801.02612 (2018)."},{"key":"e_1_2_1_106_1","unstructured":"F. Xiao L. Pei L. Chu D. Zou W. Yu Y. Zhu and T. Li. 2020. A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors. arXiv preprint arXiv:2003.01874 (2020). F. Xiao L. Pei L. Chu D. Zou W. Yu Y. Zhu and T. Li. 2020. A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors. arXiv preprint arXiv:2003.01874 (2020)."},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_26"},{"volume-title":"Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848","year":"2019","author":"Xie Qizhe","key":"e_1_2_1_108_1","unstructured":"Qizhe Xie , Zihang Dai , Eduard Hovy , Minh-Thang Luong , and Quoc V Le. 2019. Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848 ( 2019 ). Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019. Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848 (2019)."},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15986-2_16"},{"key":"e_1_2_1_110_1","first-page":"3","volume-title":"Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies (IMWUT) 2","author":"Yao S.","year":"2018","unstructured":"S. Yao , Y. Zhao , H. Shao , C. Zhang , A. Zhang , S. Hu , D. Liu , S. Liu , Lu Su , and T. Abdelzaher . 2018. Sensegan: Enabling deep learning for internet of things with a semi-supervised framework . Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies (IMWUT) 2 , 3 ( 2018 ), 1--21. S. Yao, Y. Zhao, H. Shao, C. Zhang, A. Zhang, S. Hu, D. Liu, S. Liu, Lu Su, and T. Abdelzaher. 2018. Sensegan: Enabling deep learning for internet of things with a semi-supervised framework. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies (IMWUT) 2, 3 (2018), 1--21."},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00212"},{"volume-title":"Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 199--210","author":"Young A.","key":"e_1_2_1_112_1","unstructured":"A. Young , M. Ling , and D. Arvind . 2011. IMUSim: A simulation environment for inertial sensing algorithm design and evaluation . In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 199--210 . A. Young, M. Ling, and D. Arvind. 2011. IMUSim: A simulation environment for inertial sensing algorithm design and evaluation. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 199--210."},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867733"},{"volume-title":"Proceedings of the International Conference on Ubiquitous Computing.","author":"Zhang M.","key":"e_1_2_1_114_1","unstructured":"M. Zhang and A. A. Sawchuk . 2012. USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors . In Proceedings of the International Conference on Ubiquitous Computing. M. Zhang and A. A. Sawchuk. 2012. USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In Proceedings of the International Conference on Ubiquitous Computing."},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00098"},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00917"},{"key":"e_1_2_1_117_1","doi-asserted-by":"crossref","unstructured":"Kaiyang Zhou Yongxin Yang Andrea Cavallaro and Tao Xiang. 2019. Omni-Scale Feature Learning for Person Re-Identification. In ICCV. Kaiyang Zhou Yongxin Yang Andrea Cavallaro and Tao Xiang. 2019. Omni-Scale Feature Learning for Person Re-Identification. In ICCV.","DOI":"10.1109\/ICCV.2019.00380"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12336"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_3"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3478096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T10:30:23Z","timestamp":1725791423000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478096"}},"subtitle":["Supporting Activity Recognition Training with Virtual IMU Data"],"short-title":[],"issued":{"date-parts":[[2021,9,9]]},"references-count":119,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9,9]]}},"alternative-id":["10.1145\/3478096"],"URL":"https:\/\/doi.org\/10.1145\/3478096","relation":{},"ISSN":["2474-9567"],"issn-type":[{"type":"electronic","value":"2474-9567"}],"subject":[],"published":{"date-parts":[[2021,9,9]]},"assertion":[{"value":"2021-09-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}