We introduce a novel and generic convolutional unit, DiCE unit, that is built
using dimension-wise convolutions and dimension-wise fusion. The dimension-wise
convolutions apply light-weight convolutional filtering across each dimension
of the input tensor while dimension-wise fusion efficiently combines these
dimension-wise representations; allowing the DiCE unit to efficiently encode
spatial and channel-wise information contained in the input tensor. The DiCE
unit is simple and can be seamlessly integrated with any architecture to
improve its efficiency and performance. Compared to depth-wise separable
convolutions, the DiCE unit shows significant improvements across different
architectures. When DiCE units are stacked to build the DiCENet model, we
observe significant improvements over state-of-the-art models across various
computer vision tasks including image classification, object detection, and
semantic segmentation. On the ImageNet dataset, the DiCENet delivers 2-4%
higher accuracy than state-of-the-art manually designed models (e.g.,
MobileNetv2 and ShuffleNetv2). Also, DiCENet generalizes better to tasks (e.g.,
object detection) that are often used in resource-constrained devices in
comparison to state-of-the-art separable convolution-based efficient networks,
including neural search-based methods (e.g., MobileNetv3 and MixNet. Our source
code in PyTorch is open-source and is available at
this https URL

提出了一种新型的通用卷积单元 DiCE unit，利用按维度卷积和维度融合有效地将输入张量的空间和通道信息编码，可以无缝地集成到任何架构中以提高效率和性能，在图像分类、目标检测和语义分割等各种计算机视觉任务中，DiCENet 模型比现有技术手动设计的模型具有更高的精度和更好的泛化性能。