TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding

• URL: http://arxiv.org/abs/2505.10834v1
• Date: Fri, 16 May 2025 04:03:52 GMT
• Title: TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding
• Authors: Achintha Wijesinghe, Weiwei Wang, Suchinthaka Wanninayaka, Songyang Zhang, Zhi Ding,
• Abstract summary: 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.<n>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.
• Score: 43.7367543116319
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning [67.06363342414397]
  Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages.<n>Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation.<n>We propose a self-supervised learning-based semantic communication framework (SLSCom) to enhance task inference performance.
  arXiv  Detail & Related papers  (2025-05-26T13:06:18Z)
• Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration [45.55410059471241]
  Next-generation networking shifts bit-wise data delivery to conveying semantic meanings for bandwidth efficiency.<n>This work presents a novel task-adaptive semantic communication framework based on diffusion models.<n>Test results demonstrate the efficacy of the proposed method in adaptively preserving task-relevant information for semantic communications.
  arXiv  Detail & Related papers  (2025-05-12T18:23:53Z)
• Multi-Modal Self-Supervised Semantic Communication [52.76990720898666]
  We propose a multi-modal semantic communication system that leverages multi-modal self-supervised learning to enhance task-agnostic feature extraction.<n>The proposed approach effectively captures both modality-invariant and modality-specific features while minimizing training-related communication overhead.<n>The findings underscore the advantages of multi-modal self-supervised learning in semantic communication, paving the way for more efficient and scalable edge inference systems.
  arXiv  Detail & Related papers  (2025-03-18T06:13:02Z)
• Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications [44.330805289033]
  We propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE)<n>To capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality.<n>Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.
  arXiv  Detail & Related papers  (2025-02-25T04:43:16Z)
• Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation [51.53221300103261]
  This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture.<n>A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions.<n> Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection.
  arXiv  Detail & Related papers  (2025-02-12T09:01:25Z)
• Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications [88.68461721069433]
  Next generation systems can be potentially enriched by folding message semantics and goals of communication into their design. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.
  arXiv  Detail & Related papers  (2022-07-19T16:00:57Z)
• Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework [66.81698651016444]
  A comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.
  arXiv  Detail & Related papers  (2021-11-15T19:13:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.