Bibliografski detalji
| Naslov: |
TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding |
| Autori: |
Wijesinghe, Achintha, Wang, Weiwei, Wanninayaka, Suchinthaka, Zhang, Songyang, Ding, Zhi |
| Godina izdanja: |
2025 |
| Zbirka: |
Computer Science |
| Tematski pojmovi: |
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing |
| Opis: |
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.
Comment: Submitted to the IEEE GlobeCom 2025 |
| Vrsta dokumenta: |
Working Paper |
| URL pristupa: |
http://arxiv.org/abs/2505.10834 |
| Pristupni broj: |
edsarx.2505.10834 |
| Baza podataka: |
arXiv |