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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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Zero Shot Semantic Segmentation On Pascal Voc | SOTA | HyperAI