新框架让语义通信更灵活，能自适应下游任务并高效传递关键信息。
TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding
- 引入任务自适应与上下文嵌入，联合提取关键语义
- 在多个图像任务中表现更优，带宽效率极高
- 适合动态任务场景下的高效语义传输
生成式人工智能的进展推动了下一代语义通信的发展，其核心是传递信息含义而非原始数据。语义通信的关键挑战在于准确识别并提取最关键的语义信息，同时在接收端任务变化时仍保持性能不降。本文提出一种新型语义通信框架，可联合捕捉任务特定信息与上下文信息，实现对多种下游任务的灵活适应。在主流图像数据集和计算机视觉任务上的严格实验表明，该框架相比现有方法具有显著优势：下游任务性能提升、泛化能力更强、带宽效率极高，且重建延迟极低。
原文摘要 · Abstract (English)
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 may evolve over time. To enable flexible adaptation to multiple tasks at the receiver, this work introduces a novel semantic communication framework, which is capable of jointly capturing task-specific information to enhance downstream task performance and contextual information. Through rigorous experiments on popular image datasets and computer vision tasks, our framework shows promising improvement compared to existing work, including superior performance in downstream tasks, better generalizability, ultra-high bandwidth efficiency, and low reconstruction latency.
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