Summary of Transformer-aided Semantic Communications, by Matin Mortaheb et al.
Transformer-Aided Semantic Communications
by Matin Mortaheb, Erciyes Karakaya, Mohammad A. Amir Khojastepour, Sennur Ulukus
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach for compressing and transmitting images while preserving semantic information using vision transformers. Specifically, the authors employ attention mechanisms in transformer models to identify critical segments of images and prioritize their transmission. This is achieved by creating an attention mask that highlights key objects, allowing for efficient reconstruction during the decoding phase. The proposed framework is evaluated on the TinyImageNet dataset, showcasing improved quality and accuracy compared to traditional compression methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to send pictures while keeping important details intact. It uses special computer models called vision transformers that can focus on what’s most important in an image. This helps save time and energy when sending images over limited bandwidth connections. The researchers tested this method using a big dataset of small images and found it works well, even when only a small part of the data is sent. |
Keywords
» Artificial intelligence » Attention » Mask » Transformer
