Figure 2. Overview of the proposed method. The pipeline begins by computing the cosine similarity (Cos.) between vision-language
features to obtain logits, followed by non-maximum suppression (NMS) and normalization (Norm.) to filter low-confidence patches. The
core step involves calculating the distribution discrepancy between the normalized logits and the degenerate distribution map. This solved
discrepancy is then upsampled to the original image resolution via joint bilateral upsampling (JBU) to obtain the discrepancy map. Finally,
the semantic map is generated by applying an argmax operation to the discrepancy map.