BiCrossNet: resource-efficient cross-view geolocalization with binary neural networks
This paper presents BiCrossNet, a novel approach to cross-view geolocalization utilizing binary neural networks to significantly reduce computational complexity while maintaining competitive performance. Key contributions include the development of a Bi-Gradual Unfreezing method to enhance transfer...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adfa67 |
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| Summary: | This paper presents BiCrossNet, a novel approach to cross-view geolocalization utilizing binary neural networks to significantly reduce computational complexity while maintaining competitive performance. Key contributions include the development of a Bi-Gradual Unfreezing method to enhance transfer learning, a Bi-Partitioned Optimization strategy to improve training stability, and the use of logit-based knowledge distillation to supplement standard losses. Experimental results on the University-1652 and SUES-200 datasets demonstrate that BiCrossNet establishes a new benchmark in the efficiency-performance trade-off. It achieves up to a 90.87-fold reduction in operations and uses 4.64 times less disk space compared to similar-performing state-of-the-art models on the SUES-200 dataset, and a 30-fold reduction in operations and 5.13 times less disk space on the University-1652 dataset. The code is available at https://anonymous.4open.science/r/BiCrossNet-FB7A/README.md . |
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| ISSN: | 2632-2153 |