DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative Data
Authors: Chengxiang Fan, Muzhi Zhu, Hao Chen, Yang Liu, Weijia Wu, Huaqi Zhang, Chunhua Shen

Instance segmentation is data-hungry and as model capacity increases data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require costly manual annotation limiting their data scale. Models trained on such data are prone to overfitting on the training set especially for those rare categories. While recent works have delved into exploiting generative models to create synthetic datasets for data augmentation these approaches do not efficiently harness the full potential of generative models. To address these issues we introduce a more efficient strategy to construct generative datasets for data augmentation termed DiverGen. Firstly we provide an explanation of the role of generative data from the perspective of distribution discrepancy. We investigate the impact of different data on the distribution learned by the model. We argue that generative data can expand the data distribution that the model can learn thus mitigating overfitting. Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement. On the LVIS dataset DiverGen significantly outperforms the strong model X-Paste achieving +1.1 box AP and +1.1 mask AP across all categories and +1.9 box AP and +2.5 mask AP for rare categories. Our codes are available at https://github.com/aim-uofa/DiverGen.