Zero Shot Semantic Segmentation On Coco Stuff

Métriques

Inductive Setting hIoU

Transductive Setting hIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
OTSeg+	41.5	49.8	OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
OTSeg	41.4	49.5	OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
CLIP-RC	41.2	49.7	Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation
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
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
SIGN	20.9	-	SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation
CaGNet	18.2	19.5	Context-aware Feature Generation for Zero-shot Semantic Segmentation
ZS5	15.0	16.2	Zero-Shot Semantic Segmentation
SPNet	14.0	30.3	Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
STRICT	-	34.8	A Closer Look at Self-training for Zero-Label Semantic Segmentation
MaskCLIP+	-	45.0	Extract Free Dense Labels from CLIP
MVP-SEG+	-	45.5	MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
FreeSeg	-	45.3	FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation

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Zero Shot Semantic Segmentation On Coco Stuff | SOTA | HyperAI