  Weakly supervised semantic segmentation (WSSS) with image-level labels aims
to achieve segmentation tasks without dense annotations. However, attributed to
the frequent coupling of co-occurring objects and the limited supervision from
image-level labels, the challenging co-occurrence problem is widely present and
leads to false activation of objects in WSSS. In this work, we devise a
'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of
image space and feature space. In the image space, we propose to 'separate' the
co-occurring objects with image decomposition by subdividing images into
patches. Importantly, we assign each patch a category tag from Class Activation
Maps (CAMs), which spatially helps remove the co-context bias and guide the
subsequent representation. In the feature space, we propose to 'conquer' the
false activation by enhancing semantic representation with multi-granularity
knowledge contrast. To this end, a dual-teacher-single-student architecture is
designed and tag-guided contrast is conducted, which guarantee the correctness
of knowledge and further facilitate the discrepancy among co-contexts. We
streamline the multi-staged WSSS pipeline end-to-end and tackle this issue
without external supervision. Extensive experiments are conducted, validating
the efficiency of our method and the superiority over previous single-staged
and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available
at https://github.com/zwyang6/SeCo.git.
