Summary of Videoqa-sc: Adaptive Semantic Communication For Video Question Answering, by Jiangyuan Guo et al.
VideoQA-SC: Adaptive Semantic Communication for Video Question Answering
by Jiangyuan Guo, Wei Chen, Yuxuan Sun, Jialong Xu, Bo Ai
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This paper proposes an end-to-end semantic communication (SC) system, called VideoQA-SC, specifically designed for video question answering (VideoQA) tasks. The goal is to transmit video semantics over noisy or fading wireless channels, bypassing the need for pixel-level reconstruction at the receiver. To achieve this, the authors develop a spatiotemporal semantic encoder and a learning-based bandwidth-adaptive deep joint source-channel coding scheme. Experimental results show that VideoQA-SC outperforms traditional and advanced DJSCC-based SC systems under various channel conditions and bandwidth constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video QA is all about sending video answers to questions, but how do we make sure the answers arrive correctly over noisy phone lines or internet? This paper wants to solve this problem by creating a special system that sends just the important parts of the video, not the whole thing. They use something called semantic communication, which is like using a shortcut to get to the answer instead of sending all the details. The new system they made, called VideoQA-SC, works really well and can even save a lot of bandwidth while keeping the answers correct. |
Keywords
» Artificial intelligence » Encoder » Question answering » Semantics » Spatiotemporal
