论文标题
没有更多的卷积或合并：一个新的CNN构建块，用于低分辨率图像和小物体
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects
论文作者
论文摘要
卷积神经网络（CNN）在许多计算机视觉任务（例如图像分类和对象检测）中取得了巨大的成功。但是，他们的性能在更艰巨的任务上迅速降低，因为图像是低分辨率或物体很小的。在本文中，我们指出，这根源在现有CNN体系结构中的有缺陷但常见的设计中，即使用稳固的卷积和/或汇总层，从而导致损失细粒度信息并学习较低有效的特征表示。为此，我们提出了一个新的CNN构建块，称为SPD-CONV，代替每个稳固的卷积层和每个池层（从而完全消除它们）。 SPD-CONV由一个对深度（SPD）层的层组成，然后是非构成卷积（CORS）层，并且可以在大多数CNN体系结构中应用。我们在两个最具代表性的计算机视觉任务下解释了这种新设计：对象检测和图像分类。然后，我们通过将SPD-CONV应用于Yolov5和Resnet来创建新的CNN体系结构，并从经验上表明，我们的方法显着优于最先进的深度学习模型，尤其是在具有低分辨率图像和小物体的更艰巨的任务上。我们已经在https://github.com/labsaint/spd-conv上开源代码。
Convolutional neural networks (CNNs) have made resounding success in many computer vision tasks such as image classification and object detection. However, their performance degrades rapidly on tougher tasks where images are of low resolution or objects are small. In this paper, we point out that this roots in a defective yet common design in existing CNN architectures, namely the use of strided convolution and/or pooling layers, which results in a loss of fine-grained information and learning of less effective feature representations. To this end, we propose a new CNN building block called SPD-Conv in place of each strided convolution layer and each pooling layer (thus eliminates them altogether). SPD-Conv is comprised of a space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer, and can be applied in most if not all CNN architectures. We explain this new design under two most representative computer vision tasks: object detection and image classification. We then create new CNN architectures by applying SPD-Conv to YOLOv5 and ResNet, and empirically show that our approach significantly outperforms state-of-the-art deep learning models, especially on tougher tasks with low-resolution images and small objects. We have open-sourced our code at https://github.com/LabSAINT/SPD-Conv.
