  Most existing weakly supervised semantic segmentation (WSSS) methods rely on
Class Activation Mapping (CAM) to extract coarse class-specific localization
maps using image-level labels. Prior works have commonly used an off-line
heuristic thresholding process that combines the CAM maps with off-the-shelf
saliency maps produced by a general pre-trained saliency model to produce more
accurate pseudo-segmentation labels. We propose AuxSegNet+, a weakly supervised
auxiliary learning framework to explore the rich information from these
saliency maps and the significant inter-task correlation between saliency
detection and semantic segmentation. In the proposed AuxSegNet+, saliency
detection and multi-label image classification are used as auxiliary tasks to
improve the primary task of semantic segmentation with only image-level
ground-truth labels. We also propose a cross-task affinity learning mechanism
to learn pixel-level affinities from the saliency and segmentation feature
maps. In particular, we propose a cross-task dual-affinity learning module to
learn both pairwise and unary affinities, which are used to enhance the
task-specific features and predictions by aggregating both query-dependent and
query-independent global context for both saliency detection and semantic
segmentation. The learned cross-task pairwise affinity can also be used to
refine and propagate CAM maps to provide better pseudo labels for both tasks.
Iterative improvement of segmentation performance is enabled by cross-task
affinity learning and pseudo-label updating. Extensive experiments demonstrate
the effectiveness of the proposed approach with new state-of-the-art WSSS
results on the challenging PASCAL VOC and MS COCO benchmarks.
