As convolution has empowered many smart applications, dynamic convolution
further equips it with the ability to adapt to diverse inputs. However, the
static and dynamic convolutions are either layout-agnostic or
computation-heavy, making it inappropriate for layout-specific applications,
e.g., face recognition and medical image segmentation. We observe that these
applications naturally exhibit the characteristics of large intra-image
(spatial) variance and small cross-image variance. This observation motivates
our efficient translation variant convolution (TVConv) for layout-aware visual
processing. Technically, TVConv is composed of affinity maps and a
weight-generating block. While affinity maps depict pixel-paired relationships
gracefully, the weight-generating block can be explicitly overparameterized for
better training while maintaining efficient inference. Although conceptually
simple, TVConv significantly improves the efficiency of the convolution and can
be readily plugged into various network architectures. Extensive experiments on
face recognition show that TVConv reduces the computational cost by up to 3.1x
and improves the corresponding throughput by 2.3x while maintaining a high
accuracy compared to the depthwise convolution. Moreover, for the same
computation cost, we boost the mean accuracy by up to 4.21%. We also conduct
experiments on the optic disc/cup segmentation task and obtain better
generalization performance, which helps mitigate the critical data scarcity
issue. Code is available at this https URL

本论文提出了一种有效的转换变异卷积 (TVConv), 结合亲和性图和权重生成块，可以用于面部识别和医学图像分割任务中。与深度卷积网络相比，TVConv 可以大大降低计算量并提高精度，并在视网膜图像分割任务中展现了更好的泛化性能。