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About this paper
Gaston Lenczner, Adrien Chan-Hon-Tong, Bertrand Le Saux, Nicola Luminari, Guy Le Besnerais
The DIAL framework integrates human expertise into the learning loop to efficiently refine and adapt pre-trained semantic segmentation models for remote sensing applications. It demonstrates improved model performance on new data with minimal user annotations, particularly aiding in domain adaptation and error correction.
Key insights
- Human-in-the-loop approaches effectively address data scarcity and domain adaptation challenges in remote sensing by integrating interactive and active learning.
- The 'Annotations as Channels' (AC) mechanism provides rapid local corrections without model retraining, while 'Retraining with Regularization' enables more extensive global model improvements (up to 2.5% mean IoU gain).
- Uncertainty-based active learning significantly improves annotation efficiency, with Entropy-based selection offering the best balance of speed and effectiveness for guiding user input.
Summary by alphaXiv.
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