Papers › Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

11 Jul 2024arXiv:2407.08268archive 2025-07-28

Tong Shao, Zhuotao Tian, Hang Zhao, Jingyong Su

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.

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get_image_name_list leaves162/cliptrase/clip_self_correlation.py official repository ran MIT (permissive) · c98e9a8121ce0499 · report
get_predefined_templates leaves162/cliptrase/cliptrase_d2/cliptrase/model/utils.py official repository ran MIT (permissive) · c266c766b74bafca · report
gt_transform leaves162/cliptrase/clip_self_correlation.py official repository ran MIT (permissive) · 1945566fa231150d · report
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Tasks

Language ModelingLanguage ModellingOpen Vocabulary Semantic SegmentationOpen-Vocabulary Semantic SegmentationSegmentationSemantic Segmentation

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Methods

CLIP

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