通过可学习的令牌合并来实现高效的视觉转换器
概括
本研究介绍了可学习的标记合并 (LTM) 变压器,这是视觉变压器的一个新块. 通过执行可学习的令牌合并,LTM-Transformer 提高了计算机视觉任务的效率和准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 变压器和自我注意力在深度学习中很普遍,导致计算机视觉的视觉变压器.
- 现有的视觉转换器可以是计算密集型,激励研究更高效的架构.
研究的目的:
- 提出一种新而紧的变压器块,即具有可学习令牌合并 (LTM) 的变压器.
- 降低视觉变压器的计算成本 (FLOP) 和推断时间,同时保持或提高准确性.
主要方法:
- 开发了一个可学习的代币合并方案的LTM-Transformer块.
- 获得了信息瓶 (IB) 损失的新型变化上限.
- 在LTM块内设计了一个面罩模块,以最大限度地减少衍生的IB损失上限.
主要成果:
- 在MobileViT,EfficientViT,ViT和Swin中用LTM-变压器块取代了变压器块.
- 在各种视觉变压器骨干中实现了FLOP和推断时间的显著减少.
- 在计算机视觉任务中证明了可比或改进的预测准确性.
结论:
- 通过LTM-Transformer,可以创建紧且高效的视觉变压器.
- 拟议的方法提供了一种可行的方法来提高性能和减少计算机视觉深度学习模型中的计算需求.
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