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ViT-MVT:用于多个视觉任务的统一视觉转换器网络
IEEE transactions on neural networks and learning systems
|December 21, 2023
概括
本研究介绍了ViT-MVT,这是一个用于多个视觉任务的统一网络,可以在减少存储的情况下实现高性能. 该框架支持对新任务的增量学习,而不会影响现有性能.
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:
- 传统的深度学习模型通常需要单独的网络来完成每个视觉任务,从而导致高存储需求.
- 视觉变压器 (ViT) 架构已经显示出承诺,但在同时应用于多个任务时,通常缺乏效率.
研究的目的:
- 开发一个存储效率高的统一网络,能够同时学习多个主流视觉任务.
- 挑战使用图像分类骨干用于各种视觉任务的传统方法.
主要方法:
- 开发了ViT-MVT,这是一个基于非层次ViT的框架,包含多头自我注意 (MHSE),本地感知单元 (LPU) 和本地前网络 (本地FFN).
- 实施了层级适应式共享技术,以平衡共享和任务特定的参数,以获得最佳的存储和性能.
- 利用联合任务进化搜索算法在模型大小约束下发现最佳的骨干.
主要成果:
- 在多重视觉任务中,ViT-MVT实现了最先进的性能,同时显著降低了总存储成本.
- 证明了框架对增量学习的能力,使得它能够对新任务进行概括,而不会在以前训练过的任务上降低性能.
结论:
- ViT-MVT为计算机视觉中的多任务学习提供了一个高效和有效的解决方案.
- 拟议的层级适应共享和进化搜索方法为设计多功能视觉骨干提供了一种新的方法.
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