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Training Superpixel Network Only Once
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AI
DOI：10.1109/LSP.2024.3393349.png)
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摘要
En 中文
Although existing deep superpixel methods have significantly improved the performance, they are generally dataset-specific and need re-training for unseen data. This issue results in the failure of deep superpixel methods when facing untrainable application scenarios. In this letter, we propose a novel deep superpixel algorithm to enhance the domain adaptability and generalization performance of deep superpixel methods. Specifically, we propose the domain-free embedding to replace traditional LAB color coding, reducing the network's reliance on training set statistical properties by narrowing the gap between training data and the real-world domain shift. Simultaneously, to prevent performance loss due to color information loss, we introduce the reconstructed contour constraint to directly enhance the boundary-fitting capability of superpixels. Experimental results demonstrate that our superpixel model achieve optimal cross-domain adaptation capability with just one training session, even facing extreme domain shift.
Keyword:
Generalization
segmentation
superpixel