🤖 AI Summary
In semantic segmentation, slender structures and densely packed object boundaries often suffer from ambiguous delineation, while small objects are prone to misclassification or omission. Existing distance-transform-based weighted loss functions incur substantial computational overhead and lack prediction adaptability. To address these issues, we propose Multi-scale Adaptive Weighting Loss (MAW-Loss), the first loss formulation incorporating a guided pyramid into loss design. MAW-Loss employs prediction-driven frequency-domain decomposition and cross-scale weight mapping to generate end-to-end differentiable, dynamic weight maps in real time—eliminating explicit distance transforms. Evaluated on SNEMI3D, GlaS, and DRIVE benchmarks, MAW-Loss consistently outperforms 11 state-of-the-art loss functions, achieving significant gains in Dice score and pixel accuracy. Its computational overhead is negligible, and the implementation is publicly available.
📝 Abstract
Semantic segmentation is a core task in computer vision with applications in biomedical imaging, remote sensing, and autonomous driving. While standard loss functions such as cross-entropy and Dice loss perform well in general cases, they often struggle with fine structures, particularly in tasks involving thin structures or closely packed objects. Various weight map-based loss functions have been proposed to address this issue by assigning higher loss weights to pixels prone to misclassification. However, these methods typically rely on precomputed or runtime-generated weight maps based on distance transforms, which impose significant computational costs and fail to adapt to evolving network predictions. In this paper, we propose a novel steerable pyramid-based weighted (SPW) loss function that efficiently generates adaptive weight maps. Unlike traditional boundary-aware losses that depend on static or iteratively updated distance maps, our method leverages steerable pyramids to dynamically emphasize regions across multiple frequency bands (capturing features at different scales) while maintaining computational efficiency. Additionally, by incorporating network predictions into the weight computation, our approach enables adaptive refinement during training. We evaluate our method on the SNEMI3D, GlaS, and DRIVE datasets, benchmarking it against 11 state-of-the-art loss functions. Our results demonstrate that the proposed SPW loss function achieves superior pixel precision and segmentation accuracy with minimal computational overhead. This work provides an effective and efficient solution for improving semantic segmentation, particularly for applications requiring multiscale feature representation. The code is avaiable at https://anonymous.4open.science/r/SPW-0884