The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
MobileOne: An Improved One millisecond Mobile Backbone
Open accessAbstract
Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38×faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. Code and models are available at https://github.com/apple/ml-mobileone
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Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
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Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices
Fabris, V. L., de Castro Bastos, F., de Faria, A. C. A. M., da Silva, J. V. N. A., Luiz, P. A., Custódio Silva, R., De Paris, R., & dos Santos, C. F. G. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-49018-7_29
Metadata
- Authors
- Vasu, P. K. A., Gabriel, J., Zhu, J., Tuzel, O., & Ranjan, A.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 7907-7917
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076083
- Cited by
- 394
- References
- 21
- FWCI
- 44.69
- Type
- conference paper
- Language
- en
- Open access
- green
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