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Unsupervised Semantic Segmentation With 11

Metrics

mIoU

Results

Performance results of various models on this benchmark

Paper TitleRepository
Trident70.8Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation
CLS-SEG68.7TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
ProxyCLIP65.0ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
TTD (TCL)61.1TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
TCL55.0Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
TagAlign53.9TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
TTD (MaskCLIP)43.1TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
MaskCLIP29.3Extract Free Dense Labels from CLIP
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Unsupervised Semantic Segmentation With 11 | SOTA | HyperAI