Defocus Blur Detection via Boosting Diversity of Deep Ensemble Networks.

Existing defocus blur detection (DBD) methods usually explore multi-scale and multi-level features to improve performance. However, defocus blur regions normally have incomplete semantic information, which will reduce DBD's performance if it can't be used properly. In this paper, we address the above problem by exploring deep ensemble networks, where we boost diversity of defocus blur detectors to force the network to generate diverse results that some rely more on high-level semantic information while some ones rely more on low-level information. Then, diverse result ensemble makes detection errors cancel out each other. Specifically, we propose two deep ensemble networks (e.g., adaptive ensemble network (AENet) and encoder-feature ensemble network (EFENet)), which focus on boosting diversity while costing less computation. AENet constructs different light-weight sequential adapters for one backbone network to generate diverse results without introducing too many parameters and computation. AENet is optimized only by the self- negative correlation loss. On the other hand, we propose EFENet by exploring the diversity of multiple encoded features and ensemble strategies of features (e.g., group-channel uniformly weighted average ensemble and self-gate weighted ensemble). Diversity is represented by encoded features with less parameters, and a simple mean squared error loss can achieve the superior performance. Experimental results demonstrate the superiority over the state-of-the-arts in terms of accuracy and speed. Codes and models are available at: https://github.com/wdzhao123/DENets.