在数学(特别是功能分析)中,卷积是对两个函数(f和g)的数学运算,产生三个函数,表示第一个函数的形状如何被另一个函数修改。 卷积一词既指结果函数,又指计算结果的过程。 它定义为两个函数的乘积在一个函数反转和移位后的积分。 并针对所有shift值评估积分,从而生成卷积函数。

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卷积作为CNN的基础部件之一,尽管常用,但存在两个主要缺陷:(1) 内容不可知;(2) 重度计算量。动态滤波器具有内容自适应特性,但同时进一步提升了计算量。深度(depth-wise)卷积是一种轻量型版本,但它往往会造成CNN性能下降,或者需要更大的通道数。

本文提出一种解耦动态滤波器(Decoupled Dynamic Filter),它可以同时解决上述缺陷。受启发于近期注意力的进展,DDF将深度动态滤波器解耦为空域与通道动态滤波器。该分解可以大大减少参数量,并将计算量限制在与深度卷积同等水平。同时,采用DDF替换分类网络中的标准卷积可以带来显著的性能提升。比如,ResNet50/101分别可以带来1.9%与1.3%的top1精度提升,且计算量近乎减半。在检测与联合上采样方面的实验同样证实了DDF上采样变种相比标准卷积的优异性。

本文所提DDF及其上采样变种DDF-Up具有以下几点优异属性:

Content-adaptive DDF提供了空间可变滤波器,这使得其具有内容自适应特性; Fast runtime DDF具有与深度卷积相近的计算量,因此它的推理速度要比标准卷积、动态滤波器更快; Smaller memory footprint DDF可以显著降低动态滤波器的内存占用,这使得我们可以采用DDF直接替换所有的标准卷积; Consistent performance improvements 采用DDF/DDF-Up替换标准卷积可以带来一致性的性能提升,同时在不同网络、不同任务上均取得了SOTA性能。

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Inspired by the conclusion that human choose the visual cortex regions which corresponding to the real size of the object to analyze the features of the object, when realizing the objects in the real world. This paper presents a framework -- SizeNet which based on both the real sizes and the features of objects, to solve objects recognition problems. SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18 DenseNet-121. The results show that SizeNet provides much higher accuracy rates for the objects recognition than the other algorithms. SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object. This is because SizeNet recognizes the object based not only the features, but also the real size. The real size of the object can help to exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features. SizeNet is of great significance to the study of interpretable computer vision. Our code and dataset will be made public.

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