Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation
<|reference_start|>Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation: 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.<|reference_end|>
@article{shao2024explore,
    title={Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic
  Segmentation},
    author={Tong Shao, Zhuotao Tian, Hang Zhao, Jingyong Su},
    journal={arXiv preprint arXiv:2407.08268},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08268},
    primaryClass={cs.CV}
}