  Semantic segmentation tasks based on weakly supervised condition have been
put forward to achieve a lightweight labeling process. For simple images that
only include a few categories, researches based on image-level annotations have
achieved acceptable performance. However, when facing complex scenes, since
image contains a large amount of classes, it becomes difficult to learn visual
appearance based on image tags. In this case, image-level annotations are not
effective in providing information. Therefore, we set up a new task in which
only one annotated pixel is provided for each category. Based on the more
lightweight and informative condition, a three step process is built for pseudo
labels generation, which progressively implement optimal feature representation
for each category, image inference and context-location based refinement. In
particular, since high-level semantics and low-level imaging feature have
different discriminative ability for each class under driving scenes, we divide
each category into "object" or "scene" and then provide different operations
for the two types separately. Further, an alternate iterative structure is
established to gradually improve segmentation performance, which combines
CNN-based inter-image common semantic learning and imaging prior based
intra-image modification process. Experiments on Cityscapes dataset demonstrate
that the proposed method provides a feasible way to solve weakly supervised
semantic segmentation task under complex driving scenes.
