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Open Vocabulary Semantic Segmentation On 5

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name	mIoU	Paper Title	Repository
TCL	83.2	Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs	-
MaskCLIP++	96.8	High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation	-
SCAN	97.2	Open-Vocabulary Segmentation with Semantic-Assisted Calibration	-
POMP	89.4	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition	-
OVSeg Swin-B	94.5	Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP	-
EBSeg-L	96.4	Open-Vocabulary Semantic Segmentation with Image Embedding Balancing	-
ODISE	84.6	Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models	-
ZegFormer	-	Decoupling Zero-Shot Semantic Segmentation	-
TagAlign(trained with image-text pairs)	87.9	TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification	-
ZSSeg	-	A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model	-
MAFT-ViTL	92.1	Learning Mask-aware CLIP Representations for Zero-Shot Segmentation	-
PACL	72.3	Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning	-
HyperSeg	92.1	HyperSeg: Towards Universal Visual Segmentation with Large Language Model	-
MAFT+	96.5	Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation	-
LaVG	82.5	In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation	-
SILC	97.6	SILC: Improving Vision Language Pretraining with Self-Distillation	-
FC-CLIP	95.4	Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP	-
CAT-Seg	97.0	CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation	-
MAFT-ViTL	92.1	-	-

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Open Vocabulary Semantic Segmentation On 5 | SOTA | HyperAI