An architecture for tactical intention recognition of aerial targets based on unsupervised momentum contrast and transformer
Abstract Tactical intention recognition of aerial targets is critical for battlefield decision-making, yet existing supervised deep learning approaches face fundamental challenges due to the extreme scarcity of labeled training data in military domains, where data annotation is constrained by securi...
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| Główni autorzy: | , , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
SpringerOpen
2026-03-01
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| Seria: | Journal of Big Data |
| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1186/s40537-026-01403-x |
| Etykiety: |
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