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Zero Shot Semantic Segmentation On Pascal Voc

評価指標

Inductive Setting hIoU
Transductive Setting hIoU

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
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超神経