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Fieldable Low-Light Multi-Task Perception via Foundation-Model-Assisted Domain Adaptation
Fieldable low-light perception systems require both semantic and geometric scene understanding under severe domain shift and embedded compute constraints. We present FAMDA, a foundation-model-assisted self-training framework for unsupervised domain adaptation (UDA) in multi-task dense prediction. Th
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