An optical-guided neural collapse framework derives orthogonal subspaces from optical ATR data as priors to project and regularize SAR features, achieving highest final accuracy on a 24-class benchmark with reduced performance degradation.
A Comprehensive Survey on SAR ATR in Deep-Learning Era
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A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.
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Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
An optical-guided neural collapse framework derives orthogonal subspaces from optical ATR data as priors to project and regularize SAR features, achieving highest final accuracy on a 24-class benchmark with reduced performance degradation.
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Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition
A fine-tuned large language-vision model achieves 98% accuracy on visual question answering for military vehicle identification in SAR imagery from an extended MSTAR benchmark.