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Zero Shot Semantic Segmentation On Coco Stuff

Metrics

Inductive Setting hIoU
Transductive Setting hIoU

Results

Performance results of various models on this benchmark

Paper Title
OTSeg+41.549.8OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
OTSeg41.449.5OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
CLIP-RC41.249.7Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation
ZegCLIP40.848.5ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
DeOP38.2-Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
zsseg36.341.5A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
ZegFormer33.2-Decoupling Zero-Shot Semantic Segmentation
SIGN20.9-SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation
CaGNet18.219.5Context-aware Feature Generation for Zero-shot Semantic Segmentation
ZS515.016.2Zero-Shot Semantic Segmentation
SPNet14.030.3Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
STRICT-34.8A Closer Look at Self-training for Zero-Label Semantic Segmentation
MaskCLIP+-45.0Extract Free Dense Labels from CLIP
MVP-SEG+-45.5MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
FreeSeg-45.3FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation
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