Tuning-Free Universally-Supervised Semantic Segmentation
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DOI: 10.1109/access.2024.3512379
Abstract: 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… read more here.
Keywords: segmentation; free universally; tuning free; universally supervised ... See more keywords