Scalable Video Object Segmentation with Simplified Framework

The current popular methods for video object segmentation (VOS) implement feature matching through several hand-crafted modules that separately perform feature extraction and matching. However, the above hand-crafted designs empirically cause insufficient target interaction, thus limiting the dynamic target-aware feature learning in VOS. To tackle these limitations, this paper presents a scalable Simplified VOS (SimVOS) framework to perform joint feature extraction and matching by leveraging a single transformer backbone. Specifically, SimVOS employs a scalable ViT backbone for simultaneous feature extraction and matching between query and reference features. This design enables SimVOS to learn better target-ware features for accurate mask prediction. More importantly, SimVOS could directly apply well-pretrained ViT backbones (e.g., MAE [21]) for VOS, which bridges the gap between VOS and large-scale self-supervised pre-training. To achieve a better performance-speed trade-off, we further explore within-frame attention and propose a new token refinement module to improve the running speed and save computational cost. Experimentally, our SimVOS achieves state-of-the-art results on popular video object segmentation benchmarks, i.e., DAVIS-2017 (88.0% $\mathcal{J}\& \mathcal{F}$), DAVIS-2016 (92.9% $\mathcal{J}\& \mathcal{F}$) and YouTube-VOS 2019 (84.2% $\mathcal{J}\& \mathcal{F}$), without applying any synthetic video or BL30K pre-training used in previous VOS approaches. Our code and models are available at https://github.com/jimmy-dq/SimVOS.git.