  This paper proposes a novel heterogeneous grid convolution that builds a
graph-based image representation by exploiting heterogeneity in the image
content, enabling adaptive, efficient, and controllable computations in a
convolutional architecture. More concretely, the approach builds a
data-adaptive graph structure from a convolutional layer by a differentiable
clustering method, pools features to the graph, performs a novel
direction-aware graph convolution, and unpool features back to the
convolutional layer. By using the developed module, the paper proposes
heterogeneous grid convolutional networks, highly efficient yet strong
extension of existing architectures. We have evaluated the proposed approach on
four image understanding tasks, semantic segmentation, object localization,
road extraction, and salient object detection. The proposed method is effective
on three of the four tasks. Especially, the method outperforms a strong
baseline with more than 90% reduction in floating-point operations for semantic
segmentation, and achieves the state-of-the-art result for road extraction. We
will share our code, model, and data.
