  Expanding the receptive field to capture large-scale context is key to
obtaining good performance in dense prediction tasks, such as human pose
estimation. While many state-of-the-art fully-convolutional architectures
enlarge the receptive field by reducing resolution using strided convolution or
pooling layers, the most straightforward strategy is adopting large filters.
This, however, is costly because of the quadratic increase in the number of
parameters and multiply-add operations. In this work, we explore using
learnable box filters to allow for convolution with arbitrarily large kernel
size, while keeping the number of parameters per filter constant. In addition,
we use precomputed summed-area tables to make the computational cost of
convolution independent of the filter size. We adapt and incorporate the box
filter as a differentiable module in a fully-convolutional neural network, and
demonstrate its competitive performance on popular benchmarks for the task of
human pose estimation.
