  Weakly Incremental Learning for Semantic Segmentation (WILSS) leverages a
pre-trained segmentation model to segment new classes using cost-effective and
readily available image-level labels. A prevailing way to solve WILSS is the
generation of seed areas for each new class, serving as a form of pixel-level
supervision. However, a scenario usually arises where a pixel is concurrently
predicted as an old class by the pre-trained segmentation model and a new class
by the seed areas. Such a scenario becomes particularly problematic in WILSS,
as the lack of pixel-level annotations on new classes makes it intractable to
ascertain whether the pixel pertains to the new class or not. To surmount this
issue, we propose an innovative, tendency-driven relationship of mutual
exclusivity, meticulously tailored to govern the behavior of the seed areas and
the predictions generated by the pre-trained segmentation model. This
relationship stipulates that predictions for the new and old classes must not
conflict whilst prioritizing the preservation of predictions for the old
classes, which not only addresses the conflicting prediction issue but also
effectively mitigates the inherent challenge of incremental learning -
catastrophic forgetting. Furthermore, under the auspices of this
tendency-driven mutual exclusivity relationship, we generate pseudo masks for
the new classes, allowing for concurrent execution with model parameter
updating via the resolution of a bi-level optimization problem. Extensive
experiments substantiate the effectiveness of our framework, resulting in the
establishment of new benchmarks and paving the way for further research in this
field.
