Summary of Habaek: High-performance Water Segmentation Through Dataset Expansion and Inductive Bias Optimization, by Hanseon Joo et al.
Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization
by Hanseon Joo, Eunji Lee, Minjong Cheon
First submitted to arxiv on: 21 Oct 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 proposed research aims to improve water segmentation using deep learning models, specifically upgrading the SegFormer model through data augmentation with large datasets like ADE20K and RIWA. The goal is to boost generalization and reduce processing requirements, making it suitable for real-time applications. The study examines the impact of inductive bias on attention-based models and finds that SegFormer performs better on larger datasets. To further demonstrate the effectiveness of data augmentation, the researchers employ Low-Rank Adaptation (LoRA) to lower processing complexity while maintaining accuracy. The resulting Habaek model outperforms current models in segmentation, achieving an Intersection over Union (IoU) ranging from 0.91986 to 0.94397, and superior F1-score, recall, accuracy, and precision compared to rival models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to improve water segmentation using deep learning models, making it easier for authorities to monitor rivers, lakes, and reservoirs. The researchers upgrade the SegFormer model by adding data from large datasets like ADE20K and RIWA. They find that this helps the model generalize better and work faster. To make the model even better, they use a technique called Low-Rank Adaptation (LoRA) to reduce processing time without sacrificing accuracy. The new model, Habaek, performs well in water segmentation tasks, beating current models in metrics like Intersection over Union (IoU) and F1-score. |
Keywords
» Artificial intelligence » Attention » Data augmentation » Deep learning » F1 score » Generalization » Lora » Low rank adaptation » Precision » Recall
