  CLIP, as a vision-language model, has significantly advanced Open-Vocabulary
Semantic Segmentation (OVSS) with its zero-shot capabilities. Despite its
success, its application to OVSS faces challenges due to its initial
image-level alignment training, which affects its performance in tasks
requiring detailed local context. Our study delves into the impact of CLIP's
[CLS] token on patch feature correlations, revealing a dominance of "global"
patches that hinders local feature discrimination. To overcome this, we propose
CLIPtrase, a novel training-free semantic segmentation strategy that enhances
local feature awareness through recalibrated self-correlation among patches.
This approach demonstrates notable improvements in segmentation accuracy and
the ability to maintain semantic coherence across objects.Experiments show that
we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks,
outperforming existing state-of-the-art training-free methods.The code are made
publicly available at: https://github.com/leaves162/CLIPtrase.
