  MobileNets, a class of top-performing convolutional neural network
architectures in terms of accuracy and efficiency trade-off, are increasingly
used in many resourceaware vision applications. In this paper, we present
Harmonious Bottleneck on two Orthogonal dimensions (HBO), a novel architecture
unit, specially tailored to boost the accuracy of extremely lightweight
MobileNets at the level of less than 40 MFLOPs. Unlike existing bottleneck
designs that mainly focus on exploring the interdependencies among the channels
of either groupwise or depthwise convolutional features, our HBO improves
bottleneck representation while maintaining similar complexity via jointly
encoding the feature interdependencies across both spatial and channel
dimensions. It has two reciprocal components, namely spatial
contraction-expansion and channel expansion-contraction, nested in a
bilaterally symmetric structure. The combination of two interdependent
transformations performing on orthogonal dimensions of feature maps enhances
the representation and generalization ability of our proposed module,
guaranteeing compelling performance with limited computational resource and
power. By replacing the original bottlenecks in MobileNetV2 backbone with HBO
modules, we construct HBONets which are evaluated on ImageNet classification,
PASCAL VOC object detection and Market-1501 person re-identification. Extensive
experiments show that with the severe constraint of computational budget our
models outperform MobileNetV2 counterparts by remarkable margins of at most
6.6%, 6.3% and 5.0% on the above benchmarks respectively. Code and pretrained
models are available at https://github.com/d-li14/HBONet.
