Segment objects in scene using deformable attention location over time with Truong
Segment objects in scene using deformable attention location over time with Truong
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
arXiv paper abstract https://arxiv.org/abs/2401.13937
arXiv PDF paper https://arxiv.org/pdf/2401.13937.pdf
Video object segmentation ... in computer vision ... often applied attention ... However, due to temporal changes in the video data, attention maps may not well align with the objects of interest across video frames, causing .... errors
... propose a new method for self-supervised video object segmentation based on distillation learning of deformable attention.
... devise a lightweight architecture for video object segmentation that is effectively adapted to temporal changes.
This is enabled by deformable attention mechanism, where the keys and values capturing the memory of a video sequence in the attention module have flexible locations updated across frames.
... train the proposed architecture in a self-supervised fashion through a new knowledge distillation paradigm where deformable attention maps are integrated into the distillation loss.
... method ... achieved state-of-the-art performance and optimal memory usage.
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