  This work presents a tuning-free semantic segmentation framework based on
classifying SAM masks by CLIP, which is universally applicable to various types
of supervision. Initially, we utilize CLIP's zero-shot classification ability
to generate pseudo-labels or perform open-vocabulary segmentation. However, the
misalignment between mask and CLIP text embeddings leads to suboptimal results.
To address this issue, we propose discrimination-bias aligned CLIP to closely
align mask and text embedding, offering an overhead-free performance gain. We
then construct a global-local consistent classifier to classify SAM masks,
which reveals the intrinsic structure of high-quality embeddings produced by
DBA-CLIP and demonstrates robustness against noisy pseudo-labels. Extensive
experiments validate the efficiency and effectiveness of our method, and we
achieve state-of-the-art (SOTA) or competitive performance across various
datasets and supervision types.
