核心概念
Proposing a method for self-supervised video object segmentation using distillation learning of deformable attention to address challenges in VOS.
要約
Recent techniques in computer vision have focused on attention mechanisms for object representation learning in video sequences. However, existing methods face challenges with temporal changes and computational complexity. The proposed method introduces deformable attention for adaptive spatial and temporal learning. Knowledge distillation is used to transfer learned representations from a large model to a smaller one. Extensive experiments validate the method's state-of-the-art performance and memory efficiency on benchmark datasets.
統計
Recent techniques have often applied attention mechanism to object representation learning from video sequences.
Existing techniques have utilised complex architectures, requiring highly computational complexity.
We propose a new method for self-supervised video object segmentation based on distillation learning of deformable attention.
Experimental results verify the superiority of our method via its achieved state-of-the-art performance and optimal memory usage.
引用
"We propose a new method for self-supervised video object segmentation based on distillation learning of deformable attention."
"Experimental results verify the superiority of our method via its achieved state-of-the-art performance and optimal memory usage."