Seesaw Loss for Long-Tailed Instance Segmentation (2008.10032v4)

Abstract: Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories. The overwhelming gradients of negative samples on tail classes lead to a biased learning process for classifiers. Consequently, objects of tail categories are more likely to be misclassified as backgrounds or head categories. To tackle this problem, we propose Seesaw Loss to dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor. The mitigation factor reduces punishments to tail categories w.r.t. the ratio of cumulative training instances between different categories. Meanwhile, the compensation factor increases the penalty of misclassified instances to avoid false positives of tail categories. We conduct extensive experiments on Seesaw Loss with mainstream frameworks and different data sampling strategies. With a simple end-to-end training pipeline, Seesaw Loss obtains significant gains over Cross-Entropy Loss, and achieves state-of-the-art performance on LVIS dataset without bells and whistles. Code is available at https://github.com/open-mmlab/mmdetection.

• The paper introduces Seesaw Loss, a dynamic loss function that adjusts gradients to improve segmentation accuracy on imbalanced datasets.
• It employs mitigation and compensation factors to reduce bias against tail classes during training.
• Experiments on the LVIS dataset show up to a 6.0% AP improvement, demonstrating its effectiveness in addressing class imbalance.

Seesaw Loss for Long-Tailed Instance Segmentation

The paper "Seesaw Loss for Long-Tailed Instance Segmentation" addresses the challenges faced by instance segmentation models when dealing with datasets characterized by a long-tailed distribution. In such datasets, a few head classes contain the majority of instances, while many tail classes have scarce instances. This imbalance leads to biased learning in conventional classifiers, which often misclassify tail category objects as backgrounds or head categories due to overwhelming negative gradients from the head classes.

Seesaw Loss Mechanism

To mitigate these issues, the authors propose the Seesaw Loss, a novel loss function that dynamically adjusts the gradients between positive and negative samples for each class. This is achieved through two complementary factors:

• Mitigation Factor: Reduces the penalties imposed on tail categories in relation to the ratio of instances between classes, ensuring that tail categories receive less punishment from the head categories.
• Compensation Factor: Increases the penalties for misclassified instances of tail categories to counterbalance any potential rise in false positives.

By applying these factors, Seesaw Loss effectively re-balances training, leading to improved classification accuracy for tail classes without sacrificing performance on head classes.

Experimental Results

The authors conduct extensive experiments using mainstream instance segmentation frameworks, such as Mask R-CNN and Cascade Mask R-CNN, on the LVIS dataset. The Seesaw Loss demonstrates notable improvements over the traditional Cross-Entropy Loss, achieving significant performance gains across different sampling strategies.

Numerical highlights include improvements of 6.0% in AP with a random sampler and 2.1% with a repeat factor sampler over the Cross-Entropy Loss baseline. The performance on tail classes (AP$_r$) greatly benefits from Seesaw Loss, showcasing its effectiveness in addressing class imbalance.

Implications and Future Directions

The introduction of Seesaw Loss has practical implications for real-world applications where data is inherently long-tailed, such as autonomous driving and medical imaging. The approach provides a more equitable training strategy across classes, ensuring better generalization and robustness.

Theoretically, Seesaw Loss contributes to the ongoing exploration of loss function design for imbalanced data. Its dynamic nature and independence from static distribution assumptions open avenues for further research into adaptive loss functions in AI.

Future work may explore integrating Seesaw Loss into more complex systems and further optimizing its hyperparameters to enhance performance. Additionally, its application to other tasks with imbalanced data, such as object detection and image classification, could broaden its utility in AI research.

Conclusion

The Seesaw Loss presents a promising approach to overcoming the biases introduced by long-tailed distributions in instance segmentation tasks. By effectively managing gradient imbalances, this method enhances the accuracy and reliability of segmentation models, marking a step forward in handling imbalanced datasets in AI.