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About this paper
Tomer Ronen, Omer Levy, Avram Golbert
Researchers developed Quadformer, a Vision Transformer architecture that employs a mixed-resolution tokenization scheme guided by a saliency-based Quadtree algorithm. This approach adaptively allocates computational capacity to important image regions, achieving up to 1.98 absolute percentage point accuracy improvements on ImageNet-1K compared to vanilla ViTs while controlling for computational budgets.
Key insights
- Adaptive, mixed-resolution tokenization in Vision Transformers can significantly improve accuracy and efficiency by directing computational focus to semantically important image regions.
- A feature-based saliency scorer, which estimates semantic information loss, outperforms pixel-blur based scorers in guiding adaptive resolution allocation for visual tasks.
- The uniform patch tokenization scheme of standard ViTs contains considerable redundancy, as evidenced by the high performance of oracle mixed-resolution tokenization with significantly fewer patches.
Summary by alphaXiv.
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