  In recent years, using a deep convolutional neural network (CNN) as a feature
encoder (or backbone) is the most commonly observed architectural pattern in
several computer vision methods, and semantic segmentation is no exception. The
two major drawbacks of this architectural pattern are: (i) the networks often
fail to capture small classes such as wall, fence, pole, traffic light, traffic
sign, and bicycle, which are crucial for autonomous vehicles to make accurate
decisions. (ii) due to the arbitrarily increasing depth, the networks require
massive labeled data and additional regularization techniques to converge and
to prevent the risk of over-fitting, respectively. While regularization
techniques come at minimal cost, the collection of labeled data is an expensive
and laborious process. In this work, we address these two drawbacks by
proposing a novel lightweight architecture named point-wise dense flow network
(PDFNet). In PDFNet, we employ dense, residual, and multiple shortcut
connections to allow a smooth gradient flow to all parts of the network. The
extensive experiments on Cityscapes and CamVid benchmarks demonstrate that our
method significantly outperforms baselines in capturing small classes and in
few-data regimes. Moreover, our method achieves considerable performance in
classifying out-of-the training distribution samples, evaluated on Cityscapes
to KITTI dataset.
