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About this paper
Shuoxi Zhang, Hanpeng Liu, Stephen Lin, Kun He
LaViT introduces a method to reduce the computational cost and attention saturation in Vision Transformers by re-parameterizing previously computed attention scores instead of recalculating full self-attention at every layer. This approach enables a more efficient performance-accuracy balance across image classification, object detection, and semantic segmentation tasks.
Key insights
- Full self-attention re-computation at every layer in Vision Transformers is not strictly necessary; effective attention can be re-parameterized from previously learned scores.
- Attention saturation, a common issue in deep ViTs, can be mitigated by introducing modules that allow attention to evolve more meaningfully across layers.
- Preserving the fundamental semantic properties, such as diagonality and symmetry, of attention matrices is critical when employing re-parameterization techniques.
Summary by alphaXiv.
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