  A key challenge of infrared small target segmentation (ISTS) is to balance
false negative pixels (FNs) and false positive pixels (FPs). Traditional
methods combine FNs and FPs into a single objective by weighted sum, and the
optimization process is decided by one actor. Minimizing FNs and FPs with the
same strategy leads to antagonistic decisions. To address this problem, we
propose a competitive game framework (pixelGame) from a novel perspective for
ISTS. In pixelGame, FNs and FPs are controlled by different player whose goal
is to minimize their own utility function. FNs-player and FPs-player are
designed with different strategies: One is to minimize FNs and the other is to
minimize FPs. The utility function drives the evolution of the two participants
in competition. We consider the Nash equilibrium of pixelGame as the optimal
solution. In addition, we propose maximum information modulation (MIM) to
highlight the tar-get information. MIM effectively focuses on the salient
region including small targets. Extensive experiments on two standard public
datasets prove the effectiveness of our method. Compared with other
state-of-the-art methods, our method achieves better performance in terms of
F1-measure (F1) and the intersection of union (IoU).
