Summary of Visual Language Model Based Cross-modal Semantic Communication Systems, by Feibo Jiang et al.
Visual Language Model based Cross-modal Semantic Communication Systems
by Feibo Jiang, Chuanguo Tang, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (cs.LG)
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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 proposed Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system addresses challenges in dynamic environments faced by existing Image Semantic Communication (ISC) systems. By leveraging a Cross-modal Knowledge Base (CKB), Memory-assisted Encoder and Decoder (MED), and Noise Attention Module (NAM), the VLM-CSC system successfully extracts high-density textual semantics from semantically sparse images, alleviates bandwidth pressure, and adapts to changing signal-to-noise ratios (SNR). The system’s effectiveness is validated through experimental simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of communicating information between images and text. It uses special computer models to make sure the information gets across even in difficult conditions. This helps with things like low-quality images, forgetting old information, and noisy signals. The model works by using a knowledge base to understand the image and then send it as text, using memories to keep track of what’s important, and paying attention to how well the signal is received. |
Keywords
» Artificial intelligence » Attention » Decoder » Encoder » Knowledge base » Language model » Semantics
