Most of the existing semantic segmentation approaches with image-level class
labels as supervision, highly rely on the initial class activation map (CAM)
generated from the standard classification network. In this paper, a novel
"Progressive Patch Learning" approach is proposed to improve the local details
extraction of the classification, producing the CAM better covering the whole
object rather than only the most discriminative regions as in CAMs obtained in
conventional classification models. "Patch Learning" destructs the feature maps
into patches and independently processes each local patch in parallel before
the final aggregation. Such a mechanism enforces the network to find weak
information from the scattered discriminative local parts, achieving enhanced
local details sensitivity. "Progressive Patch Learning" further extends the
feature destruction and patch learning to multi-level granularities in a
progressive manner. Cooperating with a multi-stage optimization strategy, such
a "Progressive Patch Learning" mechanism implicitly provides the model with the
feature extraction ability across different locality-granularities. As an
alternative to the implicit multi-granularity progressive fusion approach, we
additionally propose an explicit method to simultaneously fuse features from
different granularities in a single model, further enhancing the CAM quality on
the full object coverage. Our proposed method achieves outstanding performance
on the PASCAL VOC 2012 dataset e.g., with 69.6$% mIoU on the test set), which
surpasses most existing weakly supervised semantic segmentation methods. Code
will be made publicly available here this https URL

本文提出了一种新的渐进式补丁学习方法来提高弱监督语义分割中的局部细节提取，进一步将特征破坏和补丁学习扩展到多级粒度，同时与多阶段优化策略相结合来提供模型对不同程度的特征提取能力。此方法超越了大多数现有的弱监督语义分割方法，在 PASCAL VOC 2012 数据集上取得了出色的表现。