Tuning-Free Universally-Supervised Semantic Segmentation
This work presents a tuning-free semantic segmentation framework based on classifying SAM masks, which is universally applicable to various types of supervision. Initially, we utilize CLIP’s zero-shot classification ability to generate pseudo-labels or perform open-vocabulary semantic seg...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10779462/ |
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