论文标题
DSIC：用于多尺度对象检测的动态样品个性化连接器
DSIC: Dynamic Sample-Individualized Connector for Multi-Scale Object Detection
论文作者
论文摘要
尽管由于深度学习的巨大成功，对象检测已经达到了里程碑，但规模变化仍然是关键挑战。提出了集成多层次功能以减轻问题，例如经典特征金字塔网络（FPN）及其改进。但是，这些方法的专门设计的特征集成模块可能没有特征融合的最佳体系结构。此外，当用各种样品馈送时，这些模型具有固定的架构和数据流路径。他们无法调整并与每种数据兼容。为了克服上述局限性，我们提出了一个动态样本个体化连接器（DSIC），以用于多尺度对象检测。它动态调整网络连接以拟合不同的样本。特别是，DSIC由两个组成部分组成：尺度选择门（ISG）和跨尺度选择门（CSG）。 ISG从主链中自适应提取多级特征作为特征集成的输入。 CSG根据多级特征自动激活信息性数据流道路径。此外，这两个组件都是插件，并且可以嵌入任何骨架中。实验结果表明，所提出的方法的表现优于最先进的方法。
Although object detection has reached a milestone thanks to the great success of deep learning, the scale variation is still the key challenge. Integrating multi-level features is presented to alleviate the problems, like the classic Feature Pyramid Network (FPN) and its improvements. However, the specifically designed feature integration modules of these methods may not have the optimal architecture for feature fusion. Moreover, these models have fixed architectures and data flow paths, when fed with various samples. They cannot adjust and be compatible with each kind of data. To overcome the above limitations, we propose a Dynamic Sample-Individualized Connector (DSIC) for multi-scale object detection. It dynamically adjusts network connections to fit different samples. In particular, DSIC consists of two components: Intra-scale Selection Gate (ISG) and Cross-scale Selection Gate (CSG). ISG adaptively extracts multi-level features from backbone as the input of feature integration. CSG automatically activate informative data flow paths based on the multi-level features. Furthermore, these two components are both plug-and-play and can be embedded in any backbone. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts.
