Summary of Semantic Map-based Generation Of Navigation Instructions, by Chengzu Li et al.
Semantic Map-based Generation of Navigation Instructions
by Chengzu Li, Chao Zhang, Simone Teufel, Rama Sanand Doddipatla, Svetlana Stoyanchev
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 a novel approach to generating navigation instructions by framing the problem as an image captioning task using semantic maps as visual input. Traditional methods employ a sequence of panorama images, whereas our method abstracts away from visual details and fuses information into a single top-down representation, reducing computational complexity. A benchmark dataset for instruction generation using semantic maps is presented, along with an initial model and human evaluation results. While promising, the approach shows vast scope for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Navigation instructions are generated by framing the problem as an image captioning task using semantic maps as visual input. This paper proposes a new way to do this. Instead of using multiple pictures, our method uses a single map that combines information from many pictures into one. This makes it faster and more efficient. The paper also includes a special dataset for testing and training the model, and some results from people who evaluated how good the instructions are. |
Keywords
» Artificial intelligence » Image captioning
