  In computer vision, the performance of deep neural networks (DNNs) is highly
related to the feature extraction ability, i.e., the ability to recognize and
focus on key pixel regions in an image. However, in this paper, we
quantitatively and statistically illustrate that DNNs have a serious attention
bias problem on many samples from some popular datasets: (1) Position bias:
DNNs fully focus on label-independent regions; (2) Range bias: The focused
regions from DNN are not completely contained in the ideal region. Moreover, we
find that the existing self-attention modules can alleviate these biases to a
certain extent, but the biases are still non-negligible. To further mitigate
them, we propose a lightweight sub-attention strategy (LSAS), which utilizes
high-order sub-attention modules to improve the original self-attention
modules. The effectiveness of LSAS is demonstrated by extensive experiments on
widely-used benchmark datasets and popular attention networks. We release our
code to help other researchers to reproduce the results of
LSAS~\footnote{https://github.com/Qrange-group/LSAS}.
