  This paper introduces Progressively Diffused Networks (PDNs) for unifying
multi-scale context modeling with deep feature learning, by taking semantic
image segmentation as an exemplar application. Prior neural networks, such as
ResNet, tend to enhance representational power by increasing the depth of
architectures and driving the training objective across layers. However, we
argue that spatial dependencies in different layers, which generally represent
the rich contexts among data elements, are also critical to building deep and
discriminative representations. To this end, our PDNs enables to progressively
broadcast information over the learned feature maps by inserting a stack of
information diffusion layers, each of which exploits multi-dimensional
convolutional LSTMs (Long-Short-Term Memory Structures). In each LSTM unit, a
special type of atrous filters are designed to capture the short range and long
range dependencies from various neighbors to a certain site of the feature map
and pass the accumulated information to the next layer. From the extensive
experiments on semantic image segmentation benchmarks (e.g., ImageNet Parsing,
PASCAL VOC2012 and PASCAL-Part), our framework demonstrates the effectiveness
to substantially improve the performances over the popular existing neural
network models, and achieves state-of-the-art on ImageNet Parsing for large
scale semantic segmentation.
