Efficiently training accurate deep models for weakly supervised
semantic segmentation (WSSS) with image-level labels
is challenging and important. Recently, end-to-end WSSS
methods have become the focus of research due to their high
training efficiency. However, current methods suffer from
insufficient extraction of comprehensive semantic information,
resulting in low-quality pseudo-labels and sub-optimal
solutions for end-to-end WSSS. To this end, we propose a
simple and novel Self Correspondence Distillation (SCD)
method to refine pseudo-labels without introducing external
supervision. Our SCD enables the network to utilize feature
correspondence derived from itself as a distillation target,
which can enhance the network’s feature learning process by
complementing semantic information. In addition, to further
improve the segmentation accuracy, we design a Variationaware
Refine Module to enhance the local consistency of
pseudo-labels by computing pixel-level variation. Finally, we
present an efficient end-to-end Transformer-based framework
(TSCD) via SCD and Variation-aware Refine Module for the
accurate WSSS task. Extensive experiments on the PASCAL
VOC 2012 and MS COCO 2014 datasets demonstrate that our
method significantly outperforms other state-of-the-art methods.
Our code is available at https://github.com/Rongtao-
Xu/RepresentationLearning/tree/main/SCD-AAAI2023.