The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
Open accessAbstract
We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multimodal temporal-binding, i.e. the combination of modalities within a range of temporal offsets. We train the architecture with three modalities – RGB, Flow and Audio – and combine them with mid-level fusion alongside sparse temporal sampling of fused representations. In contrast with previous works, modalities are fused before temporal aggregation, with shared modality and fusion weights over time. Our proposed architecture is trained end-to-end, outperforming individual modalities as well as late-fusion of modalities. We demonstrate the importance of audio in egocentric vision, on per-class basis, for identifying actions as well as interacting objects. Our method achieves state of the art results on both the seen and unseen test sets of the largest egocentric dataset: EPIC-Kitchens, on all metrics using the public leaderboard.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Self-citationAudio-Visual Scene Analysis with Self-Supervised Multisensory Features
Owens, A., & Efros, A. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_39 · Cited by 704
Learning to Localize Sound Source in Visual Scenes
Senocak, A., Oh, T.-H., Kim, J., Yang, M.-H., & Kweon, I. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00458 · Cited by 328
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.02748 · Cited by 274
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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-citationWhat Makes Training Multi-modal Classification Networks Hard?
Wang, W., Tran, D., & Feiszli, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01271 · Cited by 588
Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
Damen, D., Doughty, H., Farinella, G. M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01531-2 · Cited by 400
Self-citationJoint Audio-Visual Deepfake Detection
Zhou, Y., & Lim, S.-N. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01453 · Cited by 217
Hear Me out: Fusional Approaches for Audio Augmented Temporal Action Localization
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Retrieval-Augmented Egocentric Video Captioning
Xu, J., Huang, Y., Hou, J., Chen, G., Zhang, Y., Feng, R., & Xie, W. (2024) · arXiv · doi:10.48550/arxiv.2401.00789 · Cited by 24
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding
Roitberg, A., Peng, K., Marinov, Z., Seibold, C., Schneider, D., & Stiefelhagen, R. (2022) · 2022 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/iv51971.2022.9827426 · Cited by 20
Continual Egocentric Activity Recognition With Foreseeable-Generalized Visual–IMU Representations
He, C., Cheng, S., Qiu, Z., Xu, L., Meng, F., Wu, Q., & Li, H. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3371975 · Cited by 11
Lifelong Learning of Large Language Model Based Agents: A Roadmap
Zheng, J., Shi, C., Cai, X., Li, Q., Zhang, D., Li, C., Yu, D., & Ma, Q. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3650546 · Cited by 7
“Ontheotherhand,Cross-ModalKnowledgeTransferinvolves applying knowledge from one modality to improve performance on tasks in another modality [60], [61], [62], [63].”
Lifelong Learning of Large Language Model based Agents: A Roadmap
Zheng, J., Shi, C., Cai, X., Li, Q., Zhang, D., Li, C., Yu, D., & Ma, Q. (2025) · arXiv · doi:10.48550/arxiv.2501.07278 · Cited by 4
Metadata
- Authors
- Kazakos, E., Nagrani, A., Zisserman, A., & Damen, D.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 5491-5500
- OpenAlex
- W2971680695
- Cited by
- 302
- References
- 5
- FWCI
- 11.89
- Type
- conference paper
- Language
- en
- Open access
- green
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