Due to the large success in object detection and instance segmentation, Mask
R-CNN attracts great attention and is widely adopted as a strong baseline for
arbitrary-shaped scene text detection and spotting. However, two issues remain
to be settled. The first is dense text case, which is easy to be neglected but
quite practical. There may exist multiple instances in one proposal, which
makes it difficult for the mask head to distinguish different instances and
degrades the performance. In this work, we argue that the performance
degradation results from the learning confusion issue in the mask head. We
propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask
head, which alleviates the issue and promotes robustness significantly. And we
propose instance-aware mask learning in which the mask head learns to predict
the shape of the whole instance rather than classify each pixel to text or
non-text. With instance-aware mask learning, the mask branch can learn
separated and compact masks. The second is that due to large variations in
scale and aspect ratio, RPN needs complicated anchor settings, making it hard
to maintain and transfer across different datasets. To settle this issue, we
propose an adaptive label assignment in which all instances especially those
with extreme aspect ratios are guaranteed to be associated with enough anchors.
Equipped with these components, the proposed method named MAYOR achieves
state-of-the-art performance on five benchmarks including DAST1500, MSRA-TD500,
ICDAR2015, CTW1500, and Total-Text.

本文针对 Mask R-CNN 在场景文本检测与定位中面临的实际问题，提出了一种基于 MLP 解码器和实例感知掩模学习技术的方法，可以显著提高鲁棒性。同时提出了一种自适应标签分配方法，以应对比例和宽高比差异较大的实例问题。该方法在多个基准测试中展现了优异的性能。