[41] Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore. Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl., 12(2):74-82, mar 2011. 2
[42] Heike Leutheuser, Dominik Schuldhaus, and Bjoern M. Eskofier. Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset. PLOS ONE, 8(10):1-11, 10 2013. 2
[43] Yiming Li, Ziang Cao, Andrew Liang, Benjamin Liang, Luoyao Chen, Hang Zhao, and Chen Feng. Egocentric prediction of action target in 3d. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. 1
[44] Shaowei Liu, Subarna Tripathi, Somdeb Majumdar, and Xiaolong Wang. Joint hand motion and interaction hotspots prediction from egocentric videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3282-3292, June 2022. 1
[45] Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu. 3d-to-2d distillation for indoor scene parsing. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4462–4472, 2021. 3
[46] Ilya Loshchilov and Frank Hutter. Fixing weight decay regularization in adam, 2018. 6
[47] Niall Lyons, Avik Santra, and Ashutosh Pandey. Improved deep representation learning for human activity recognition using imu sensors. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 326-332, 2021. 2
[48] Sharmin Majumder and Nasser Kehtarnavaz. Vision and inertial sensing fusion for human action recognition: A review. IEEE Sensors Journal, 21(3):2454-2467, 2021. 2
[49] Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris. Ar-net: Adaptive frame resolution for efficient action recognition. arXiv preprint arXiv:2007.15796, 2020. 3, 4
[50] Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, and Rogerio Feris. Adafuse: Adaptive temporal fusion network for efficient action recognition. In International Conference on Learning Representations, 2021. 2, 3, 7, 9
[51] Rameswar Panda, Chun-Fu Chen, Quanfu Fan, Xineng Sun, Kate Saenko, Aude Oliva, and Rogerio Feris. AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition. In International Conference on Computer Vision (ICCV), 2021. 2, 4
[52] Philip R.L. Parker, Morgan A. Brown, Matthew C. Smear, and Christopher M. Niell. Movement-related signals in sensory areas: Roles in natural behavior. Trends in Neurosciences, 43(8):581-595, 2020. 1
[53] Philip R. L. Parker, Elliott T. T. Abe, Emmalyn S. P. Leonard, Dylan M. Martins, and Christopher M. Niell. Joint coding of visual input and eye/head position in v1 of freely moving mice. bioRxiv, 2022. 1
[54] Mandela Patrick, Dylan Campbell, Yuki M. Asano, Ishan Misra Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and João F. Henriques. Keeping your eye on the ball: Trajectory attention in video transformers. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 1, 2, 3, 4, 6, 8, 9
[55] Chiara Plizzari, Mirco Planamente, Gabriele Goletto, Marco Cannici, Emanuele Gusso, Matteo Matteucci, and Barbara Caputo. E2(go)motion: Motion augmented event stream for egocentric action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19935-19947, June 2022. 1
[56] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Charades-ego: A large-scale dataset of paired third and first person videos. arXiv preprint arXiv:1804.09626, 2018. 1
[57] Karen Simonyan and Andrew Zisserman. Two-stream convolutional networks for action recognition in videos. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 27. Curran Associates, Inc., 2014. 2
[58] Shuyang Sun, Zhanghui Kuang, Lu Sheng, Wanli Ouyang, and Wei Zhang. Optical flow guided feature: A fast and robust motion representation for video action recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 2
[59] Yin Tang, Lei Zhang, Fuhong Min, and Jun He. Multi-scale deep feature learning for human activity recognition using wearable sensors. IEEE Transactions on Industrial Electronics, pages 1-1, 2022. 2
[60] Wenjin Tao, Haodong Chen, Md. Moniruzzaman, Ming C. Leu, Zhaozheng Yi, and Ruwen Qin. Attention-based sensor fusion for human activity recognition using imu signals. ArXiv, abs/2112.11224, 2021. 2
[61] Catherine Tong, Jinchen Ge, and Nicholas D Lane. Zero-shot learning for imu-based activity recognition using video embeddings. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(4):1-23, 2021. 2
[62] Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6450-6459, 2018. 3
[63] Satoshi Tsutsui, Ruta Desai, and Karl Ridgeway. How you move your head tells what you do: Self-supervised video representation learning with egocentric cameras and imu sensors. ICCV Workshop on Egocentric Perception, Interaction and Computing (EPIC), 2021. 3
[64] Jindong Wang, Yiqiang Chen, Shuju Hao, Xiaohui Peng, and Lisha Hu. Deep learning for sensor-based activity recognition: A survey. Pattern Recognit. Lett., 119:3-11, 2019. 2