Fetching the paper…
Reading the bibliography…
Pre-training representations acquired via self-supervised learning could achieve high accuracy on even tasks with small training data.
M. Bachlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Troster, “Wearable assistant for parkinson’s disease patients with the freezing of gait symptom,” IEEE Transactions on Information Technology in Biomedicine , vol. 14, no. 2, pp. 436–446, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SigKDD Explorations Newsletter , vol. 12, no. 2, pp. 74–82, 2011
2011
Earlier work this paper cites.
S. Kwon, J. Lee, G. S. Chung, and K. S. Park, “Validation of heart rate extraction through an iphone accelerometer,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society . IEEE, 2011, pp. 5260–5263
2011
Earlier work this paper cites.
R. Chavarriaga, H. Sagha, A. Calatroni, S. T. Digumarti, G. Tröster, J. d. R. Millán, and D. Roggen, “The opportunity challenge: A benchmark database for on-body sensor-based activity recognition,” Pattern Recognition Letters , vol. 34, no. 15, pp. 2033–2042, 2013
2013
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
W. Jiang and Z. Yin, “Human activity recognition using wearable sensors by deep convolutional neural networks,” in Proceedings of the 23rd ACM international conference on Multimedia , 2015, pp. 1307–1310
2015
Earlier work this paper cites.
M. A. Alsheikh, A. Selim, D. Niyato, L. Doyle, S. Lin, and H.-P. Tan, “Deep activity recognition models with triaxial accelerometers,” in Workshops at the Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,” Communications of the ACM , vol. 59, no. 2, pp. 64–73, 2016
2016
Earlier work this paper cites.
D. Ravi, C. Wong, B. Lo, and G.-Z. Yang, “Deep learning for human activity recognition: A resource efficient implementation on low-power devices,” in 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN) . IEEE, 2016, pp. 71–76
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
L. C. González, R. Moreno, H. J. Escalante, F. Martínez, and M. R. Carlos, “Learning roadway surface disruption patterns using the bag of words representation,” IEEE Transactions on Intelligent Transportation Systems , vol. 18, no. 11, pp. 2916–2928, 2017
2017
Earlier work this paper cites.
A. Doherty, D. Jackson, N. Hammerla, T. Plötz, P. Olivier, M. H. Granat, T. White, V. T. Van Hees, M. I. Trenell, C. G. Owen et al. , “Large scale population assessment of physical activity using wrist worn accelerometers: the uk biobank study,” PloS one , vol. 12, no. 2, p. e0169649, 2017
2017
Earlier work this paper cites.
E. Casilari, J. A. Santoyo-Ramón, and J. M. Cano-García, “Umafall: A multisensor dataset for the research on automatic fall detection,” Procedia Computer Science , vol. 110, pp. 32–39, 2017
2017
Earlier work this paper cites.
T. T. Um, F. M. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kulić, “Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks,” in Proceedings of the 19th ACM international conference on multimodal interaction , 2017, pp. 216–220
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
2017
Earlier work this paper cites.
J. W. Kamminga, D. V. Le, J. P. Meijers, H. Bisby, N. Meratnia, and P. J. Havinga, “Robust sensor-orientation-independent feature selection for animal activity recognition on collar tags,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 2, no. 1, pp. 1–27, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Willetts, S. Hollowell, L. Aslett, C. Holmes, and A. Doherty, “Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 uk biobank participants,” Scientific reports , vol. 8, no. 1, p. 7961, 2018
2018
Earlier work this paper cites.
T. Hur, J. Bang, T. Huynh-The, J. Lee, J.-I. Kim, and S. Lee, “Iss2image: A novel signal-encoding technique for cnn-based human activity recognition,” Sensors , vol. 18, no. 11, p. 3910, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3733–3742
2018
Cited alongside, same era.
M. R. Carlos, L. C. González, J. Wahlström, G. Ramírez, F. Martínez, and G. Runger, “How smartphone accelerometers reveal aggressive driving behavior?—the key is the representation,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 8, pp. 3377–3387, 2019
2019
Cited alongside, same era.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in neural information processing systems , vol. 33, pp. 21 271–21 284, 2020
2020
Later among the works it cites.
S. Chan Chang, R. Walmsley, J. Gershuny, T. Harms, E. Thomas, K. Milton, P. Kelly, C. Foster, A. Wong, N. Gray et al. , “Capture-24: Activity tracker dataset for human activity recognition,” 2021
2021
Later among the works it cites.
S. Changpinyo, P. Sharma, N. Ding, and R. Soricut, “Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3558–3568
2021
Later among the works it cites.
S. Azizi, B. Mustafa, F. Ryan, Z. Beaver, J. Freyberg, J. Deaton, A. Loh, A. Karthikesalingam, S. Kornblith, T. Chen et al. , “Big self-supervised models advance medical image classification,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3478–3488
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. I. M. Ismail, R. A. Dziyauddin, N. A. A. Salleh, F. Muhammad-Sukki, N. A. Bani, M. A. M. Izhar, and L. A. Latiff, “A review of vibration detection methods using accelerometer sensors for water pipeline leakage,” IEEE access , vol. 7, pp. 51 965–51 981, 2019
2019
Cited alongside, same era.
G. Brunner, D. Melnyk, B. Sigfússon, and R. Wattenhofer, “Swimming style recognition and lap counting using a smartwatch and deep learning,” in Proceedings of the 2019 ACM International Symposium on Wearable Computers , 2019, pp. 23–31
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Saeed, T. Ozcelebi, and J. Lukkien, “Multi-task self-supervised learning for human activity detection,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 3, no. 2, pp. 1–30, 2019
2019
Cited alongside, same era.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data , vol. 6, no. 1, pp. 1–48, 2019
2019
Cited alongside, same era.
V. Radu and M. Henne, “Vision2sensor: Knowledge transfer across sensing modalities for human activity recognition,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 3, no. 3, pp. 1–21, 2019
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, “Big self-supervised models are strong semi-supervised learners,” Advances in neural information processing systems , vol. 33, pp. 22 243–22 255, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
S. Shin, J. Kim, Y. Yu, S. Lee, and K. Lee, “Self-supervised transfer learning from natural images for sound classification,” Applied Sciences , vol. 11, no. 7, p. 3043, 2021
2021
Later among the works it cites.
X. Chen and K. He, “Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 15 750–15 758
2021
Later among the works it cites.
X. Chen, S. Xie, and K. He, “An empirical study of training self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9640–9649
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Khaertdinov, E. Ghaleb, and S. Asteriadis, “Contrastive self-supervised learning for sensor-based human activity recognition,” in 2021 IEEE International Joint Conference on Biometrics (IJCB) . IEEE, 2021, pp. 1–8
2021
Later among the works it cites.
H. Haresamudram, I. Essa, and T. Plötz, “Contrastive predictive coding for human activity recognition,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 5, no. 2, pp. 1–26, 2021
2021
Later among the works it cites.
H. Xu, P. Zhou, R. Tan, M. Li, and G. Shen, “Limu-bert: Unleashing the potential of unlabeled data for imu sensing applications,” in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems , 2021, pp. 220–233
2021
Later among the works it cites.
S. Bhalla, M. Goel, and R. Khurana, “Imu2doppler: Cross-modal domain adaptation for doppler-based activity recognition using imu data,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 5, no. 4, pp. 1–20, 2021
2021
Later among the works it cites.
2022
Closest in time.
H. Haresamudram, I. Essa, and T. Plötz, “Assessing the state of self-supervised human activity recognition using wearables,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 6, no. 3, pp. 1–47, 2022
2022
Closest in time.
X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, “Scaling vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 104–12 113
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
J. Shin, S. Lee, T. Gong, H. Yoon, H. Roh, A. Bianchi, and S.-J. Lee, “Mydj: Sensing food intakes with an attachable on your eyeglass frame,” in CHI Conference on Human Factors in Computing Systems , 2022, pp. 1–17
2022
Closest in time.
Y. Jain, C. I. Tang, C. Min, F. Kawsar, and A. Mathur, “Collossl: Collaborative self-supervised learning for human activity recognition,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 6, no. 1, pp. 1–28, 2022
2022
Closest in time.
S. Deldari, H. Xue, A. Saeed, D. V. Smith, and F. D. Salim, “Cocoa: Cross modality contrastive learning for sensor data,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 6, no. 3, pp. 1–28, 2022
2022
Closest in time.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al. , “Palm: Scaling language modeling with pathways,” Journal of Machine Learning Research , vol. 24, no. 240, pp. 1–113, 2023
2023
Closest in time.