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Zero Shot Semantic Segmentation On Coco Stuff

Métriques

Inductive Setting hIoU

Transductive Setting hIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Inductive Setting hIoU	Transductive Setting hIoU	Paper Title	Repository
ZegCLIP	40.8	48.5	ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
DeOP	38.2	-	Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
CaGNet	18.2	19.5	Context-aware Feature Generation for Zero-shot Semantic Segmentation
STRICT	-	34.8	A Closer Look at Self-training for Zero-Label Semantic Segmentation
OTSeg	41.4	49.5	OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
SPNet	14.0	30.3	Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
MaskCLIP+	-	45.0	Extract Free Dense Labels from CLIP
CLIP-RC	41.2	49.7	Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation
MVP-SEG+	-	45.5	MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation	-
zsseg	36.3	41.5	A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
ZegFormer	33.2	-	Decoupling Zero-Shot Semantic Segmentation
OTSeg+	41.5	49.8	OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
FreeSeg	-	45.3	FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation	-
SIGN	20.9	-	SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation	-
ZS5	15.0	16.2	Zero-Shot Semantic Segmentation

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