Active Learning for Image Segmentation in Underwater Inspection

Alapfogalmak

Active learning with epistemic uncertainty is effective for image segmentation in underwater inspection tasks, significantly reducing the required training data and lowering costs.

Kivonat

The content discusses the application of active learning with epistemic uncertainty for image segmentation in underwater inspection tasks. It explores the use of mutual information as an acquisition function and evaluates the effectiveness of this approach using DenseNet and HyperSeg models on datasets like CamVid and real underwater images. The results show that active learning can achieve high meanIoU performance with a small percentage of data, particularly beneficial for unbalanced datasets like those in underwater inspections.

Directory:

1. Abstract
   • Active learning aims to minimize training data while maintaining model performance.
2. Introduction
   • Computer vision's role in automation across various applications.
3. Methodology
   • Use of DenseNet and HyperSeg models for segmentation.
4. Results
   • Performance comparison between models trained with active learning vs random selection.
5. Conclusion
   • Demonstrated effectiveness of active learning with epistemic uncertainty in reducing data requirements for image segmentation.

Statisztikák

For the pipeline dataset, HyperSeg achieved 67.5% meanIoU using 12.5% of the data.
Epistemic uncertainty was calculated using mutual information as an acquisition function.

Idézetek

"Annotating large datasets is time-consuming, expensive, or infeasible."
"Using active learning can significantly lower costs in underwater inspection tasks."

Főbb Kivonatok

Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection

by Luiza Ribeir... : arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14002.pdf

Mélyebb kérdések

How can active learning be applied to other domains beyond computer vision

Active learning can be applied to various domains beyond computer vision, such as natural language processing, healthcare, finance, and cybersecurity. In natural language processing, active learning can help improve machine translation models by selecting the most informative text samples for training. In healthcare, active learning can assist in medical image analysis tasks like tumor detection or disease classification by prioritizing the labeling of critical images. For financial institutions, active learning can optimize fraud detection systems by focusing on suspicious transactions that require human validation. In cybersecurity, active learning can enhance threat detection algorithms by identifying novel attack patterns through iterative model training with selected data points.

What are potential drawbacks or limitations of using epistemic uncertainty for image selection

While using epistemic uncertainty for image selection offers several benefits in improving model performance and reducing annotation efforts, there are potential drawbacks and limitations to consider:

Computational Overhead: Calculating epistemic uncertainty using methods like Monte Carlo Dropout may increase computational complexity and inference time.
Hyperparameter Sensitivity: The effectiveness of uncertainty-based selection heavily relies on setting appropriate thresholds and parameters like the number of forward passes (T) in MC-Dropout.
Limited Generalization: Models trained with uncertain samples may focus too much on specific instances rather than capturing broader patterns in the data distribution.
Data Quality Dependency: Epistemic uncertainty is influenced by data quality issues such as noise or bias which might impact its reliability for sample selection.

How might advancements in active learning impact industries reliant on expert knowledge

Advancements in active learning have the potential to significantly impact industries reliant on expert knowledge:

Reduced Annotation Costs: By selectively choosing which data points to label based on uncertainty estimates, industries requiring expert annotations (e.g., medical imaging or legal document review) can reduce labeling costs while maintaining high model accuracy.
Improved Model Performance: Active learning ensures that models are trained on diverse and informative samples leading to better generalization capabilities even with limited labeled data.
Enhanced Decision-Making Processes: Industries such as finance or autonomous driving benefit from more accurate predictions enabled by actively selecting crucial training examples during model development.
Faster Innovation Cycles: With reduced reliance on exhaustive manual labeling processes due to smart sample selection strategies offered by active learning frameworks, industries can iterate faster towards improved solutions without compromising quality standards.