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Tongji University, Laboratoire IBISC, Univ-Evry, Universit茅 Paris-Saclay
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VLMs struggle to reason about visual scenes in adverse weather, losing significant segmentation accuracy as rain, snow, or fog intensifies.
Forget exhaustive enumeration: a Transformer-based reinforcement learning approach can efficiently optimize sequential service region design under uncertainty, outperforming standard DRL methods.
