Temel Kavramlar
OneVOS proposes a unified framework for Video Object Segmentation using an All-in-One Transformer, achieving state-of-the-art performance across various datasets.
Özet
Abstract: OneVOS introduces a novel framework that unifies VOS components with an All-in-One Transformer.
Introduction: Discusses the importance of VOS in video analysis and the limitations of existing methods.
Method: Details the architecture of OneVOS, including Mask Embedding, All-in-One Transformer, and Unidirectional Hybrid Attention.
Experiment: Shows quantitative comparisons on various datasets, highlighting the superior performance of OneVOS.
Conclusion: Concludes by emphasizing the significance of OneVOS in advancing Video Object Segmentation.
İstatistikler
OneVOS achieves 70.1% J&F score on LVOS dataset.
Extensive experiments demonstrate superiority across 7 datasets.
Alıntılar
"OneVOS demonstrates a substantial performance advantage even in more challenging scenarios."
"Our model registers a 2.45% improvement in performance over the enhanced baseline."