The DFB framework can be extended to handle more complex and diverse out-of-distribution (OOD) datasets by incorporating several strategies that address significant domain shifts. First, the framework could integrate domain adaptation techniques that allow the model to learn invariant features across different domains. This could involve using adversarial training methods, where a domain discriminator is employed to ensure that the learned representations are robust to domain shifts.
Additionally, the DFB framework could benefit from multi-task learning, where auxiliary tasks are introduced to help the model generalize better across varying distributions. For instance, incorporating tasks that focus on background feature learning from diverse datasets could enhance the model's ability to distinguish between in-distribution and OOD samples effectively.
Another approach is to enhance the pseudo segmentation mask generation process by utilizing more sophisticated segmentation techniques, such as deep learning-based segmentation models that can adapt to different domains. This would allow the DFB framework to better capture the nuances of foreground and background features in datasets with significant domain shifts.
Finally, the framework could be augmented with ensemble methods that combine predictions from multiple models trained on different subsets of the data. This would provide a more robust OOD detection mechanism by leveraging the strengths of various models to handle the complexities of diverse OOD datasets.