The paper "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" by Gohnaz Ghiasi et al. explores the effectiveness of the Copy-Paste data augmentation technique in instance segmentation. Copy-Paste involves randomly pasting objects from one image onto another, which is a simple and effective method that does not require complex context modeling. The authors find that this simple mechanism can significantly improve the performance of instance segmentation models, both in terms of data efficiency and accuracy. Key findings include: - **Data Efficiency**: Copy-Paste, combined with large-scale jittering, improves data efficiency by up to 2 times compared to standard scale jittering. - **Accuracy**: On the COCO dataset, Copy-Paste achieves 49.1 mask AP and 57.3 box AP, outperforming previous state-of-the-art methods by +0.6 mask AP and +1.5 box AP. - **Semi-Supervised Learning**: Copy-Paste is additive with semi-supervised methods, such as self-training, which leverage pseudo-labels from unlabeled data. - **Long-Tail Visual Recognition**: Copy-Paste shows significant improvements on datasets with a long-tail distribution, such as LVIS, particularly for rare object categories. The paper also discusses the robustness of Copy-Paste across different training configurations, including backbone initialization, training schedules, and image resolutions. Additionally, it demonstrates that Copy-Paste can be easily integrated into existing instance segmentation codebases and does not introduce additional training or inference overheads. Overall, the study highlights the effectiveness and simplicity of Copy-Paste as a data augmentation technique, making it a valuable addition to the toolkit for improving instance segmentation models.The paper "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" by Gohnaz Ghiasi et al. explores the effectiveness of the Copy-Paste data augmentation technique in instance segmentation. Copy-Paste involves randomly pasting objects from one image onto another, which is a simple and effective method that does not require complex context modeling. The authors find that this simple mechanism can significantly improve the performance of instance segmentation models, both in terms of data efficiency and accuracy. Key findings include: - **Data Efficiency**: Copy-Paste, combined with large-scale jittering, improves data efficiency by up to 2 times compared to standard scale jittering. - **Accuracy**: On the COCO dataset, Copy-Paste achieves 49.1 mask AP and 57.3 box AP, outperforming previous state-of-the-art methods by +0.6 mask AP and +1.5 box AP. - **Semi-Supervised Learning**: Copy-Paste is additive with semi-supervised methods, such as self-training, which leverage pseudo-labels from unlabeled data. - **Long-Tail Visual Recognition**: Copy-Paste shows significant improvements on datasets with a long-tail distribution, such as LVIS, particularly for rare object categories. The paper also discusses the robustness of Copy-Paste across different training configurations, including backbone initialization, training schedules, and image resolutions. Additionally, it demonstrates that Copy-Paste can be easily integrated into existing instance segmentation codebases and does not introduce additional training or inference overheads. Overall, the study highlights the effectiveness and simplicity of Copy-Paste as a data augmentation technique, making it a valuable addition to the toolkit for improving instance segmentation models.