UniFormer:统一卷积和自我注意力用于视觉识别
概括
我们介绍UniFormer,这是一款新的视觉转换器,它结合了卷积和自我注意力,以有效地从图像和视频中学习. 这种统一的框架在各种视觉任务中取得了最先进的结果,证明了其在表示学习中的有效性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 在局部特征提取方面表现出色,但在全球依赖性方面扎.
- 视觉转换器 (ViT) 捕获远程依赖,但可能是多余的.
- 现有的模型在平衡局部冗余性和视觉数据的全球依赖性方面面临挑战.
研究的目的:
- 提出一种新的统一变压器 (UniFormer),它结合了CNN和ViT的优势.
- 开发一个高效和有效的框架,用于学习视觉表现.
- 解决现有模型在捕获本地和全球信息方面的局限性.
主要方法:
- UniFormer采用了一个变压器架构,在浅层和深层中使用关系聚合器,以捕捉本地和全球代币亲和力.
- 该模型无地整合了卷积和自我注意力机制.
- 为了提高效率,采用了简洁的沙钟设计,具有令牌缩小和恢复.
主要成果:
- 在没有额外的训练数据的情况下,UniFormer在ImageNet-1K分类上实现了86.3%的top-1精度.
- 在各种下游任务中获得了最先进的性能,包括视频分类 (Kinetics-400/600,Something-Something V1/V2),对象检测 (COCO),语义细分 (ADE20K) 和姿势估计 (COCO).
- 一个高效的UniFormer变体显示出比轻型模型高出2-4倍的吞吐量.
结论:
- 通过统一本地和全球特征提取,UniFormer有效地学习歧视性表示.
- 拟议的模型为各种计算机视觉应用提供了强大而通用的骨干.
- UniFormer为开发高效和高性能视觉模型提供了一个有前途的方向.
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