TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding
Achintha Wijesinghe, Weiwei Wang, Suchinthaka Wanninayaka, Songyang Zhang, Zhi Ding
生成式人工智能的最新进展引入了突破性的方法来创新下一代语义通信,该方法优先考虑传达信息的含义,而不仅仅是传输原始数据。 语义通信的一个基本挑战在于准确识别和提取最关键的语义信息,同时适应下游任务而不会降低性能,特别是当接收者的目标可能随着时间的推移而演变时。 为了灵活地适应接收方的多个任务,这项工作引入了一种新的语义通信框架,它能够共同捕获特定任务的信息,以提高下游任务性能和上下文信息。 通过对流行的图像数据集和计算机视觉任务的严格实验,我们的框架与现有工作相比显示出有希望的改进,包括下游任务的卓越性能,更好的可推广性,超高带宽效率和低重建延迟。
Recent advancements in generative artificial intelligence have introduced groundbreaking approaches to innovating next-generation semantic communication, which prioritizes conveying the meaning of a message rather than merely transmitting raw data. A fundamental challenge in semantic communication lies in accurately identifying and extracting the most critical semantic information while adapting to downstream tasks without degrading performance, particularly when the objective at the receiver ma...