  Semantic segmentation is a hot topic in computer vision where the most
challenging tasks of object detection and recognition have been handling by the
success of semantic segmentation approaches. We propose a concept of
object-by-object learning technique to detect 11 types of facial skin lesions
using semantic segmentation methods. Detecting individual skin lesion in a
dense group is a challenging task, because of ambiguities in the appearance of
the visual data. We observe that there exist co-occurrent visual relations
between object classes (e.g., wrinkle and age spot, or papule and whitehead,
etc.). In fact, rich contextual information significantly helps to handle the
issue. Therefore, we propose REthinker blocks that are composed of the locally
constructed convLSTM/Conv3D layers and SE module as a one-shot attention
mechanism whose responsibility is to increase network's sensitivity in the
local and global contextual representation that supports to capture ambiguously
appeared objects and co-occurrence interactions between object classes.
Experiments show that our proposed model reached MIoU of 79.46% on the test of
a prepared dataset, representing a 15.34% improvement over Deeplab v3+ (MIoU of
64.12%).
