Summary of Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass Llm Limitations in Urban Environments, by Ibne Farabi Shihab et al.
Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments
by Ibne Farabi Shihab, Sudesh Ramesh Bhagat, Anuj Sharma
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 comparison between a robust ensemble model and ONE-PEACE Large Language Model (LLM) for detecting sidewalks accurately. The authors evaluate their approach on Cityscapes, Ade20k, and Boston Dataset, showing that the ensemble model outperforms individual models with mean Intersection Over Union (mIOU) scores of 93.1%, 90.3%, and 90.6% respectively under ideal conditions. The study also demonstrates the robustness of the ensemble model in noisy conditions like Salt-and-Pepper and Speckle noise, while ONE-PEACE LLM suffers from a significant decline in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two models for detecting sidewalks accurately. It shows that one model works better than another on certain datasets. The first model is an ensemble of smaller models, which does well even when there is noise or distortion in the images. The second model is called ONE-PEACE LLM, which performs well but gets worse with noisy conditions. |
Keywords
* Artificial intelligence * Ensemble model * Large language model
