  Weakly supervised semantic segmentation with only image-level labels aims to
reduce annotation costs for the segmentation task. Existing approaches
generally leverage class activation maps (CAMs) to locate the object regions
for pseudo label generation. However, CAMs can only discover the most
discriminative parts of objects, thus leading to inferior pixel-level pseudo
labels. To address this issue, we propose a saliency guided Inter- and
Intra-Class Relation Constrained (I$^2$CRC) framework to assist the expansion
of the activated object regions in CAMs. Specifically, we propose a saliency
guided class-agnostic distance module to pull the intra-category features
closer by aligning features to their class prototypes. Further, we propose a
class-specific distance module to push the inter-class features apart and
encourage the object region to have a higher activation than the background.
Besides strengthening the capability of the classification network to activate
more integral object regions in CAMs, we also introduce an object guided label
refinement module to take a full use of both the segmentation prediction and
the initial labels for obtaining superior pseudo-labels. Extensive experiments
on PASCAL VOC 2012 and COCO datasets demonstrate well the effectiveness of
I$^2$CRC over other state-of-the-art counterparts. The source codes, models,
and data have been made available at
\url{https://github.com/NUST-Machine-Intelligence-Laboratory/I2CRC}.
