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Unsupervised Semantic Segmentation With 4

Metrics

Mean IoU (val)

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Trident	26.7	Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation
ProxyCLIP	24.2	ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
COSMOS ViT-B/16	17.7	COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
TagAlign	17.3	TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
TCL	17.1	Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
TTD (TCL)	17.0	TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
CLIPpy ViT-B	13.5	Perceptual Grouping in Contrastive Vision-Language Models
TTD (MaskCLIP)	12.7	TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
ReCo	11.2	ReCo: Retrieve and Co-segment for Zero-shot Transfer
MaskCLIP	9.8	Extract Free Dense Labels from CLIP
GroupViT (RedCaps)	9.2	GroupViT: Semantic Segmentation Emerges from Text Supervision

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Unsupervised Semantic Segmentation With 4 | SOTA | HyperAI