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MasQCLIP for Open-Vocabulary Universal Image Segmentation

2023-01-01 · ICCV 2023 1 · Xin Xu, Tianyi Xiong, Zheng Ding, Zhuowen Tu

We present a new method for open-vocabulary universal image segmentation, which is capable of performing instance, semantic, and panoptic segmentation under a unified framework. Our approach, called MasQCLIP, seamlessly integrates with a pre-trained CLIP model by utilizing its dense features, thereby circumventing the need for extensive parameter training. MasQCLIP emphasizes two new aspects when building an image segmentation method with a CLIP model: 1) a student-teacher module to deal with masks of the novel (unseen) classes by distilling information from the base (seen) classes; 2) a fine-tuning process to update model parameters for the queries Q within the CLIP model. Thanks to these two simple and intuitive designs, MasQCLIP is able to achieve state-of-the-art performances with a substantial gain over the competing methods by a large margin across all three tasks, including open-vocabulary instance, semantic, and panoptic segmentation. Project page is at https://masqclip.github.io/.

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Code (1)

mlpc-ucsd/MasQCLIP 공식 구현 pytorch

Tasks

Image SegmentationPanoptic SegmentationSegmentationSemantic Segmentation

Methods 이 논문이 사용한 방법론

BASE 설명 없음
CLIP Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning…

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