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Unsupervised Semantic Segmentation With 4

Metrics

Mean IoU (val)

Results

Performance results of various models on this benchmark

Paper Title
Trident26.7Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation
ProxyCLIP24.2ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
COSMOS ViT-B/1617.7COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
TagAlign17.3TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
TCL17.1Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
TTD (TCL)17.0TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
CLIPpy ViT-B13.5Perceptual Grouping in Contrastive Vision-Language Models
TTD (MaskCLIP)12.7TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
ReCo11.2ReCo: Retrieve and Co-segment for Zero-shot Transfer
MaskCLIP9.8Extract Free Dense Labels from CLIP
GroupViT (RedCaps)9.2GroupViT: Semantic Segmentation Emerges from Text Supervision
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Unsupervised Semantic Segmentation With 4 | SOTA | HyperAI