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Open Vocabulary Semantic Segmentation On 7

Métriques

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	mIoU	Paper Title	Repository
MaskCLIP	10	Open-Vocabulary Universal Image Segmentation with MaskCLIP	-
SILC	25.8	SILC: Improving Vision Language Pretraining with Self-Distillation	-
MAFT+	21.6	Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation	-
MAFT-ViTL	15.7	Learning Mask-aware CLIP Representations for Zero-Shot Segmentation	-
MaskCLIP++	23.9	High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation	-
ODISE	14.5	Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models	-
SED	22.6	SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation	-
EBSeg-L	21.0	Open-Vocabulary Semantic Segmentation with Image Embedding Balancing	-
Mask-Adapter	22.7	Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation	-
SCAN	16.7	Open-Vocabulary Segmentation with Semantic-Assisted Calibration	-
CAT-Seg	23.8	CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation	-
FC-CLIP	18.2	Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP	-
OVSeg Swin-B	12.4	Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP	-

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Open Vocabulary Semantic Segmentation On 7 | SOTA | HyperAI