Continuous convolution has recently gained prominence due to its ability to
handle irregularly sampled data and model long-term dependency. Also, the
promising experimental results of using large convolutional kernels have
catalyzed the development of continuous convolution since they can construct
large kernels very efficiently. Leveraging neural networks, more specifically
multilayer perceptrons (MLPs), is by far the most prevalent approach to
implementing continuous convolution. However, there are a few drawbacks, such
as high computational costs, complex hyperparameter tuning, and limited
descriptive power of filters. This paper suggests an alternative approach to
building a continuous convolution without neural networks, resulting in more
computationally efficient and improved performance. We present self-moving
point representations where weight parameters freely move, and interpolation
schemes are used to implement continuous functions. When applied to construct
convolutional kernels, the experimental results have shown improved performance
with drop-in replacement in the existing frameworks. Due to its lightweight
structure, we are first to demonstrate the effectiveness of continuous
convolution in a large-scale setting, e.g., ImageNet, presenting the
improvements over the prior arts. Our code is available on
this https URL

本文提出了一种不需要神经网络的连续卷积建模方法，通过自动移动的点表示和插值方案实现连续函数，从而构建卷积核，其计算效率更高、性能更好，并在 ImageNet 等大规模数据集上得到了验证。