The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Open accessAbstract
Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale remote gaze-tracking dataset and method for robust 3D gaze estimation in unconstrained images. Our dataset consists of 238 subjects in indoor and outdoor environments with labelled 3D gaze across a wide range of head poses and distances. It is the largest publicly available dataset of its kind by both subject and variety, made possible by a simple and efficient collection method. Our proposed 3D gaze model extends existing models to include temporal information and to directly output an estimate of gaze uncertainty. We demonstrate the benefits of our model via an ablation study, and show its generalization performance via a cross-dataset evaluation against other recent gaze benchmark datasets. We furthermore propose a simple self-supervised approach to improve cross-dataset domain adaptation. Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting. Our dataset and models will be made available at http://gaze360.csail.mit.edu.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 2×
“As evidenced by the success of 2D body and face tracking models in the wild [2], to improve in-the-wild robustness it is important to collect data with a large number of different subjects, large variation in natural illumination and a wide range of head poses and gaze directions.”
BackgroundAdversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
Koenker, R. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511754098 · Cited by 4,551
Wei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966
RMPE: Regional Multi-person Pose Estimation
Fang, H.-S., Xie, S., Tai, Y.-W., & Lu, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.256 · Cited by 1,884
DensePose: Dense Human Pose Estimation in the Wild.
Güler, R. A., Neverova, N., & Kokkinos, I. (2018) · UCL Discovery (University College London) · Cited by 1,421
Temporal Relational Reasoning in Videos
Zhou, B., Andonian, A., Oliva, A., & Torralba, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01246-5_49 · Cited by 1,067
Self-citationBidirectional LSTM Networks for Improved Phoneme Classification and Recognition
Graves, A., Fernández, S., & Schmidhuber, J. (2005) · Lecture Notes in Computer Science · doi:10.1007/11550907_126 · Cited by 925
Cited by
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630
Self-citationStrazdas, D., Hintz, J., Khalifa, A., Abdelrahman, A. A., Hempel, T., & Al-Hamadi, A. (2022) · Sensors · doi:10.3390/s22030923 · Cited by 53
Metadata
- Authors
- Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., & Torralba, A.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 6911-6920
- OpenAlex
- W2981785948
- Cited by
- 470
- References
- 29
- FWCI
- 23.24
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., & Torralba, A. (2019). Gaze360: Physically Unconstrained Gaze Estimation in the Wild. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00701
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