  The labor-intensive labeling for semantic segmentation has spurred the
emergence of Unsupervised Semantic Segmentation. Recent studies utilize
patch-wise contrastive learning based on features from image-level
self-supervised pretrained models. However, relying solely on similarity-based
supervision from image-level pretrained models often leads to unreliable
guidance due to insufficient patch-level semantic representations. To address
this, we propose a Progressive Proxy Anchor Propagation (PPAP) strategy. This
method gradually identifies more trustworthy positives for each anchor by
relocating its proxy to regions densely populated with semantically similar
samples. Specifically, we initially establish a tight boundary to gather a few
reliable positive samples around each anchor. Then, considering the
distribution of positive samples, we relocate the proxy anchor towards areas
with a higher concentration of positives and adjust the positiveness boundary
based on the propagation degree of the proxy anchor. Moreover, to account for
ambiguous regions where positive and negative samples may coexist near the
positiveness boundary, we introduce an instance-wise ambiguous zone. Samples
within these zones are excluded from the negative set, further enhancing the
reliability of the negative set. Our state-of-the-art performances on various
datasets validate the effectiveness of the proposed method for Unsupervised
Semantic Segmentation.
