Summary of When the Small-loss Trick Is Not Enough: Multi-label Image Classification with Noisy Labels Applied to Cctv Sewer Inspections, by Keryan Chelouche et al.
When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
by Keryan Chelouche, Marie Lachaize, Marine Bernard, Louise Olgiati, Remi Cuingnet
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper tackles the problem of label noise in multi-label image classification (MLC), a crucial aspect in automating Closed-Circuit Television (CCTV) inspections for maintaining sewerage networks. The authors adapt three successful single-label classification (SLC) methods to MLC, showcasing that hybrid sample selection approaches are more effective in handling complex label noise. They develop a novel method, MHSS (Multi-label Hybrid Sample Selection), which outperforms existing solutions in dealing with both synthetic and real-world noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sewer pipe inspections more efficient by addressing a big problem called “label noise”. Label noise is when data is wrong or noisy, making it hard to train good models. The researchers take three successful methods that work well for one type of data (single-label) and adapt them to work with another type of data (multi-label). They find that combining these methods gives even better results than using just one method alone. This is an important step towards making sewer pipe inspections more automated and efficient. |
Keywords
» Artificial intelligence » Classification » Image classification
