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Referring Expression Segmentation On Refcocog 1

Metrics

Overall IoU

Results

Performance results of various models on this benchmark

		Paper Title	Repository
UniLSeg-100	80.54	Universal Segmentation at Arbitrary Granularity with Language Instruction
MLCD-Seg-7B	80.5	Multi-label Cluster Discrimination for Visual Representation Learning
UniLSeg-20	79.47	Universal Segmentation at Arbitrary Granularity with Language Instruction
HyperSeg	78.9	HyperSeg: Towards Universal Visual Segmentation with Large Language Model
EVF-SAM	77.4	EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
C3VG	76.39	Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
DETRIS	75.3	Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
GROUNDHOG	74.6	GROUNDHOG: Grounding Large Language Models to Holistic Segmentation	-
MaskRIS (Swin-B, combined DB)	71.09	MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
SafaRi-B	71.06	SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation	-
PolyFormer-L	70.19	PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
PolyFormer-B	69.05	PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
MaskRIS (Swin-B)	66.5	MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
MagNet	66.03	Mask Grounding for Referring Image Segmentation
LAVT (Swin-B)	62.09	LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
VLT (Darknet53)	56.65	Vision-Language Transformer and Query Generation for Referring Segmentation
VATEX	-	Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding

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Referring Expression Segmentation On Refcocog 1 | SOTA | HyperAI