  Box-supervised instance segmentation has recently attracted lots of research
efforts while little attention is received in aerial image domain. In contrast
to the general object collections, aerial objects have large intra-class
variances and inter-class similarity with complex background. Moreover, there
are many tiny objects in the high-resolution satellite images. This makes the
recent pairwise affinity modeling method inevitably to involve the noisy
supervision with the inferior results. To tackle these problems, we propose a
novel aerial instance segmentation approach, which drives the network to learn
a series of level set functions for the aerial objects with only box
annotations in an end-to-end fashion. Instead of learning the pairwise
affinity, the level set method with the carefully designed energy functions
treats the object segmentation as curve evolution, which is able to accurately
recover the object's boundaries and prevent the interference from the
indistinguishable background and similar objects. The experimental results
demonstrate that the proposed approach outperforms the state-of-the-art
box-supervised instance segmentation methods. The source code is available at
https://github.com/LiWentomng/boxlevelset.
