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The current popular methods for video object segmentation (VOS) implementfeature matching through several hand-crafted modules that separately performfeature extraction and matching. However, the above hand-crafted designsempirically cause insufficient target interaction, thus limiting the dynamictarget-aware feature learning in VOS. To tackle these limitations, this paperpresents a scalable Simplified VOS (SimVOS) framework to perform joint featureextraction and matching by leveraging a single transformer backbone.Specifically, SimVOS employs a scalable ViT backbone for simultaneous featureextraction and matching between query and reference features. This designenables SimVOS to learn better target-ware features for accurate maskprediction. More importantly, SimVOS could directly apply well-pretrained ViTbackbones (e.g., MAE) for VOS, which bridges the gap between VOS andlarge-scale self-supervised pre-training. To achieve a better performance-speedtrade-off, we further explore within-frame attention and propose a new tokenrefinement module to improve the running speed and save computational cost.Experimentally, our SimVOS achieves state-of-the-art results on popular videoobject segmentation benchmarks, i.e., DAVIS-2017 (88.0% J&F), DAVIS-2016 (92.9%J&F) and YouTube-VOS 2019 (84.2% J&F), without applying any synthetic video orBL30K pre-training used in previous VOS approaches.

Scalable Video Object Segmentation with Simplified Framework