HyperAI
الرئيسيةالأخبارأحدث الأوراق البحثيةالدروسمجموعات البياناتالموسوعةSOTAنماذج LLMلوحة الأداء GPUالفعاليات
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HyperAI

Zero Shot Semantic Segmentation On Coco Stuff

المقاييس

Inductive Setting hIoU
Transductive Setting hIoU

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
Inductive Setting hIoU
Transductive Setting hIoU
Paper TitleRepository
ZegCLIP40.848.5ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
DeOP38.2-Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
CaGNet18.219.5Context-aware Feature Generation for Zero-shot Semantic Segmentation
STRICT-34.8A Closer Look at Self-training for Zero-Label Semantic Segmentation
OTSeg41.449.5OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
SPNet14.030.3Semantic Projection Network for Zero- and Few-Label Semantic Segmentation
MaskCLIP+-45.0Extract Free Dense Labels from CLIP
CLIP-RC41.249.7Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation
MVP-SEG+-45.5MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation-
zsseg36.341.5A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
ZegFormer33.2-Decoupling Zero-Shot Semantic Segmentation
OTSeg+41.549.8OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
FreeSeg-45.3FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation-
SIGN20.9-SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation-
ZS515.016.2Zero-Shot Semantic Segmentation
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