  Dilated convolutions are widely used in deep semantic segmentation models as
they can enlarge the filters' receptive field without adding additional weights
nor sacrificing spatial resolution. However, as dilated convolutional filters
do not possess positional knowledge about the pixels on semantically meaningful
contours, they could lead to ambiguous predictions on object boundaries. In
addition, although dilating the filter can expand its receptive field, the
total number of sampled pixels remains unchanged, which usually comprises a
small fraction of the receptive field's total area. Inspired by the Lateral
Inhibition (LI) mechanisms in human visual systems, we propose the dilated
convolution with lateral inhibitions (LI-Convs) to overcome these limitations.
Introducing LI mechanisms improves the convolutional filter's sensitivity to
semantic object boundaries. Moreover, since LI-Convs also implicitly take the
pixels from the laterally inhibited zones into consideration, they can also
extract features at a denser scale. By integrating LI-Convs into the Deeplabv3+
architecture, we propose the Lateral Inhibited Atrous Spatial Pyramid Pooling
(LI-ASPP), the Lateral Inhibited MobileNet-V2 (LI-MNV2) and the Lateral
Inhibited ResNet (LI-ResNet). Experimental results on three benchmark datasets
(PASCAL VOC 2012, CelebAMask-HQ and ADE20K) show that our LI-based segmentation
models outperform the baseline on all of them, thus verify the effectiveness
and generality of the proposed LI-Convs.
