用注意力辅助的特征比较和特征增强用于无类计数
Liang Dong1, Yian Yu1, Di Zhang1
1College of Information Engineering, Shenyang University, Shenyang 110044, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
ACECount是一个新的阶级不可知计数 (CAC) 框架,它使用注意力和CNN来进行少数射击计数 (FSC). 它显著减少了看不见的对象类上的错误,超过了现有的方法.
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:
- 类无意识计数 (CAC) 旨在使用少数实例计数对象.
- 短拍计数 (FSC) 将此扩展到以前未见的对象类.
- 现有的方法面临着一般化和规模变化的挑战.
研究的目的:
- 引入ACECount,这是一个新的FSC框架.
- 为了提高未见对象类的实例计数精度.
- 在各种数据集中展示强大的性能.
主要方法:
- 使用一个结合注意力机制和卷积神经网络 (CNN) 的框架.
- 采用交叉注意力查询图像-示例相似性识别.
- 包含一个特征注意模块和一个多尺度回归头,用于增强特征表示和尺度变化处理.
主要成果:
- 在FSC-147数据集中实现了显著的平均绝对误差 (MAE) 减少 (0.3在验证时,0.26在测试时).
- 在类特定计数 (CSC) 任务中表现强.
- 在人群和车辆计数数据集上表现优于领先的FSC算法 (GMN,FamNet,SAFECount,LOCA,SPDCN).
结论:
- ACECount 提供了一种强大的解决方案,可以应对短暂的计数挑战.
- 该框架展示了出色的数据集概括能力.
- ACECount推进了无类和少量射击对象计数的最先进技术.
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