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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

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