MADTP++:弥合令牌和重量修剪之间的差距,以加速VLT
IEEE transactions on pattern analysis and machine intelligence
|January 6, 2026
概括
MADTP++提供了一个统一的框架,可以通过同时修剪令牌和权重来压缩视觉语言转换器 (VLT). 这种方法可以显著降低计算成本和模型参数,同时保持性能.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 视觉语言转换器 (VLT) 显示出巨大的潜力,但却面临着高计算成本.
- 目前用于VLT的压缩方法有限,经常忽视交叉模式对齐和动态压缩需求.
研究的目的:
- 开发一个新的,统一的框架,以有效压缩视觉语言转换器.
- 解决现有方法在处理标记和重量修剪同时处理的局限性.
主要方法:
- 拟议的MADTP++,将多模式对齐指导 (MAG) 和动态令牌修剪 (DTP) 集成用于令牌压缩.
- 引入了硬件意识的重量修剪 (HWP),使用稀疏张力芯进行细粒度重量修剪.
- 实施了合作优化培训策略,使用知识蒸约束来进行联合优化.
主要成果:
- MADTP++显著降低了模型参数和计算成本 (GFLOP).
- 与现有的VLT压缩技术相比,该方法实现了更高的压缩.
- 实验表明,在各种VLT模型和数据集中保持竞争性表现.
结论:
- MADTP++为压缩视觉语言转换器提供了一种有效和统一的方法.
- 该框架可以在不影响模型性能的情况下实现显著的效率提升.
- 拟议的方法为VLT模型优化提供了一个灵活且对硬件有意识的解决方案.
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