Enhancing F&B Separation with Clustering-Assisted WTAL

In this article, we propose a novel approach to separating foreground (F) and background (B) regions in video frames using a Clustering-Assisted F&B SEparation (CASE) network. Our approach builds upon a standard WTAL baseline, which provides a primary estimation of F&B snippets, and then introduces a clustering-based F&B separation algorithm to refine the separation.
The clustering component divides the snippets into multiple clusters, while the classifier component classifies each cluster as either foreground or background. However, since no ground-truth labels are available to train these components, we propose a unified self-labeling mechanism to generate high-quality pseudo-labels for them.
Our proposed approach provides several benefits over traditional context-based methods, including the ability to handle multiple latent groups and provide a more comprehensive description of both the foreground and background distributions. Additionally, our approach is robust to different numbers of clusters (K) and easy to tune an appropriate K in practice.
In conclusion, the CASE network offers a novel and effective solution for separating F and B regions in video frames, leveraging clustering and self-labeling techniques to improve the accuracy and efficiency of the separation process.