Neural networks based on convolutional operations have achieved remarkable
results in the field of deep learning, but there are two inherent flaws in
standard convolutional operations. On the one hand, the convolution operation
be confined to a local window and cannot capture information from other
locations, and its sampled shapes is fixed. On the other hand, the size of the
convolutional kernel is fixed to k $\times$ k, which is a fixed square shape,
and the number of parameters tends to grow squarely with size. It is obvious
that the shape and size of targets are various in different datasets and at
different locations. Convolutional kernels with fixed sample shapes and squares
do not adapt well to changing targets. In response to the above questions, the
Alterable Kernel Convolution (AKConv) is explored in this work, which gives the
convolution kernel an arbitrary number of parameters and arbitrary sampled
shapes to provide richer options for the trade-off between network overhead and
performance. In AKConv, we define initial positions for convolutional kernels
of arbitrary size by means of a new coordinate generation algorithm. To adapt
to changes for targets, we introduce offsets to adjust the shape of the samples
at each position. Moreover, we explore the effect of the neural network by
using the AKConv with the same size and different initial sampled shapes.
AKConv completes the process of efficient feature extraction by irregular
convolutional operations and brings more exploration options for convolutional
sampling shapes. Object detection experiments on representative datasets
COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of
AKConv. AKConv can be used as a plug-and-play convolutional operation to
replace convolutional operations to improve network performance. The code for
the relevant tasks can be found at this https URL

基于卷积操作的神经网络在深度学习领域取得了显著的成果，但标准卷积操作存在两个固有缺陷，本文介绍了 Alterable Kernel Convolution（AKConv）。AKConv 通过给卷积核赋予任意数量的参数和任意采样形状，在网络开销与性能之间提供更丰富的权衡选择，通过不规则卷积操作完成高效特征提取过程，改进了目标检测任务的网络性能。