Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
This work addresses video object segmentation for computer vision applications, offering an incremental improvement over existing methods by integrating foreground and background features.
This paper tackles semi-supervised video object segmentation by proposing a collaborative approach that equally treats foreground and background embeddings, resulting in improved segmentation performance. The method achieves state-of-the-art results of 82.9% and 82.8% on DAVIS and YouTube-VOS benchmarks without using simulated pre-training data.
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. CFBI separates the feature embedding into the foreground object region and its corresponding background region, implicitly promoting them to be more contrastive and improving the segmentation results accordingly. Moreover, CFBI performs both pixel-level matching processes and instance-level attention mechanisms between the reference and the predicted sequence, making CFBI robust to various object scales. Based on CFBI, we introduce a multi-scale matching structure and propose an Atrous Matching strategy, resulting in a more robust and efficient framework, CFBI+. We conduct extensive experiments on two popular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulated data for pre-training, our CFBI+ achieves the performance (J&F) of 82.9% and 82.8%, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.