Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
This addresses a bottleneck in semantic segmentation training for computer vision researchers, offering a practical enhancement to existing methods.
The paper tackles the inflexibility of Intersection over Union (IoU) losses in handling soft labels for semantic segmentation, introducing Jaccard Metric Losses (JMLs) that enable techniques like label smoothing and knowledge distillation, resulting in consistent accuracy improvements across 4 datasets and 13 architectures, with significant outperformance over state-of-the-art methods.
Intersection over Union (IoU) losses are surrogates that directly optimize the Jaccard index. Leveraging IoU losses as part of the loss function have demonstrated superior performance in semantic segmentation tasks compared to optimizing pixel-wise losses such as the cross-entropy loss alone. However, we identify a lack of flexibility in these losses to support vital training techniques like label smoothing, knowledge distillation, and semi-supervised learning, mainly due to their inability to process soft labels. To address this, we introduce Jaccard Metric Losses (JMLs), which are identical to the soft Jaccard loss in standard settings with hard labels but are fully compatible with soft labels. We apply JMLs to three prominent use cases of soft labels: label smoothing, knowledge distillation and semi-supervised learning, and demonstrate their potential to enhance model accuracy and calibration. Our experiments show consistent improvements over the cross-entropy loss across 4 semantic segmentation datasets (Cityscapes, PASCAL VOC, ADE20K, DeepGlobe Land) and 13 architectures, including classic CNNs and recent vision transformers. Remarkably, our straightforward approach significantly outperforms state-of-the-art knowledge distillation and semi-supervised learning methods. The code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.