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 weakly supervised
semantic segmentation (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 to guarantee the correctness of
knowledge and further facilitate the discrepancy among co-occurring objects. We
streamline the multi-staged WSSS pipeline end-to-end and tackle co-occurrence
without external supervision. Extensive experiments are conducted, validating
the efficiency of our method tackling co-occurrence and the superiority over
previous single-staged and even multi-staged competitors on PASCAL VOC and MS
COCO. Code will be available.

通过图像空间和特征空间从分割图像中的共现对象中分离出对象，并通过增强语义表示来解决共现问题。