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Unsupervised Semantic Segmentation With 11

Métriques

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	mIoU	Paper Title	Repository
TTD (TCL)	61.1	TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
TCL	55.0	Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
CLS-SEG	68.7	TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
Trident	70.8	Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation
MaskCLIP	29.3	Extract Free Dense Labels from CLIP
TagAlign	53.9	TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
TTD (MaskCLIP)	43.1	TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
ProxyCLIP	65.0	ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

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