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By distilling visual foundation models, this work achieves a significant leap in event stream representation learning, surpassing prior methods in generalization, data efficiency, and transferability.
Semantic communication doesn't have to be computationally expensive: this paper introduces a framework that explicitly trades off rate, semantic distortion, and model complexity, enabling efficient system design.
