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arXiv · 2412.11464

High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation

Quan-Sheng Zeng·Yunheng Li·Daquan Zhou·Guanbin Li·Qibin Hou·Ming-Ming Cheng
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Abstract

Open-vocabulary image segmentation has been advanced through the synergy between mask generators and vision-language models like Contrastive Language-Image Pre-training (CLIP). Previous approaches focus on generating masks while aligning mask features with text embeddings during training. In this paper, we observe that relying on generated low-quality masks can weaken the alignment of vision and language in regional representations. This motivates us to present a new fine-tuning framework, named MaskCLIP++, which uses ground-truth masks instead of generated masks to enhance the mask classification capability of CLIP. Due to the limited diversity of image segmentation datasets with mask annotations, we propose incorporating a consistency alignment principle during fine-tuning, which alleviates categorical bias toward the fine-tuning dataset. After low-cost fine-tuning, MaskCLIP++ significantly improves the mask classification performance on multi-domain datasets. Combining with the mask generator in previous state-of-the-art mask-based open vocabulary segmentation methods, we achieve performance improvements of +1.7, +2.3, +2.1, +3.1, and +0.3 mIoU on the A-847, PC-459, A-150, PC-59, and PAS-20 datasets, respectively. Code is avaliable at https://github.com/HVision-NKU/MaskCLIPpp .

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Quan-Sheng Zeng, Yunheng Li, Daquan Zhou, Guanbin Li, Qibin Hou, Ming-Ming Cheng. 2025-03-12. High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation. https://arxiv.org/abs/2412.11464

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