  In this paper, we address the challenge of segmenting global contaminants in
large images. The precise delineation of such structures requires ample global
context alongside understanding of textural patterns. CNNs specialise in the
latter, though their ability to generate global features is limited. Attention
measures long range dependencies in images, capturing global context, though at
a large computational cost. We propose a gridded attention mechanism to address
this limitation, greatly increasing efficiency by processing multi-scale
features into smaller tiles. We also enhance the attention mechanism for
increased sensitivity to texture orientation, by measuring correlations across
features dependent on different orientations, in addition to channel and
positional attention. We present results on a new dataset of astronomical
images, where the task is segmenting large contaminating dust clouds.
