# Efficient Semantic Communication Through Transformer-Aided Compression

arXiv: https://arxiv.org/abs/2412.01817 · HTML: https://mlsanity.com/paper/2412.01817/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects](https://mlsanity.com/paper/2212.09668/) (2022)
- [Transformer-aided Wireless Image Transmission with Channel Feedback](https://mlsanity.com/paper/2306.09101/) (2023)
- [Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning](https://mlsanity.com/paper/2408.08670/) (2024)
- [Transformer-Aided Semantic Communications](https://mlsanity.com/paper/2405.01521/) (2024)
- [Semantic Multi-Resolution Communications](https://mlsanity.com/paper/2308.11604/) (2023)

## Built on by (most cited first, 3 of 3)

- [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (2025)
- [Multi-Modal Semantic Communication](https://mlsanity.com/paper/2512.15691/) (2025)
- [Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission](https://mlsanity.com/paper/2609.16279/) (2026)

## Abstract (arXiv)

Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to address the time-varying channels in wireless communication systems. In this work, we introduce a channel-aware adaptive framework for semantic communication, where different regions of the image are encoded and compressed based on their semantic content. By employing vision transformers, we interpret the attention mask as a measure of the semantic contents of the patches and dynamically categorize the patches to be compressed at various rates as a function of the instantaneous channel bandwidth. Our method enhances communication efficiency by adapting the encoding resolution to the content's relevance, ensuring that even in highly constrained environments, critical information is preserved. We evaluate the proposed adaptive transmission framework using the TinyImageNet dataset, measuring both reconstruction quality and accuracy. The results demonstrate that our approach maintains high semantic fidelity while optimizing bandwidth, providing an effective solution for transmitting multi-resolution data in limited bandwidth conditions.