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

• URL: http://arxiv.org/abs/2407.08268v1
• Date: Thu, 11 Jul 2024 08:12:16 GMT
• Title: Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation
• Authors: Tong Shao, Zhuotao Tian, Hang Zhao, Jingyong Su,
• Abstract summary: We propose CLIPtrase, a training-free semantic segmentation strategy. It enhances local feature awareness through recalibrated self-correlation among patches. Experiments show that we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks.
• Score: 38.16802763051431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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