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Zero Shot Semantic Segmentation On Pascal Voc

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 TitleRepository
CLIP-RC88.493.0Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation
MaskCLIP+-87.4Extract Free Dense Labels from CLIP
ZegCLIP84.391.1ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
DeOp80.8-Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
SPNet-38.8Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
CaGNet-43.7Context-aware Feature Generation for Zero-shot Semantic Segmentation
OTSeg84.594.2OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
FreeSeg-86.9FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation-
OTSeg+87.494.4OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
zsseg77.579.3A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
ZegFormer73.3-Decoupling Zero-Shot Semantic Segmentation
ZS5-33.8Zero-Shot Semantic Segmentation
STRICT-49.8A Closer Look at Self-training for Zero-Label Semantic Segmentation
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