TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding

TL;DR

This paper introduces TACO, a semantic communication framework that adapts to multiple tasks by embedding context and task-specific information, improving efficiency and performance in generative AI-driven communication systems.

Contribution

The work proposes a novel framework that jointly captures task-specific and contextual information for flexible, efficient semantic communication adaptable to evolving downstream tasks.

Findings

Improves downstream task performance

Enhances generalizability across tasks

Achieves ultra-high bandwidth efficiency

Abstract

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.