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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