Referring Expression Segmentation On Refcoco 3

Metrics

Overall IoU

Results

Performance results of various models on this benchmark

		Paper Title	Repository
MLCD-Seg-7B	79.4	Multi-label Cluster Discrimination for Visual Representation Learning
HyperSeg	79.0	HyperSeg: Towards Universal Visual Segmentation with Large Language Model
DETRIS	75.2	Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
EVF-SAM	75.2	EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
C3VG	74.68	Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
HIPIE	73.9	Hierarchical Open-vocabulary Universal Image Segmentation
UniLSeg-100	73.18	Universal Segmentation at Arbitrary Granularity with Language Instruction
UniLSeg-20	72.70	Universal Segmentation at Arbitrary Granularity with Language Instruction
UNINEXT-H	72.47	Universal Instance Perception as Object Discovery and Retrieval
SafaRi-B	70.78	SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation	-
GROUNDHOG	70.5	GROUNDHOG: Grounding Large Language Models to Holistic Segmentation	-
MaskRIS (Swin-B, combined DB)	70.26	MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
GLEE-Pro	69.6	General Object Foundation Model for Images and Videos at Scale
PolyFormer-L	69.33	PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
PolyFormer-B	67.64	PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
MaskRIS (Swin-B)	67.54	MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
MagNet	66.16	Mask Grounding for Referring Image Segmentation
ReLA	66.04	GRES: Generalized Referring Expression Segmentation
VLT	63.53	VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
CRIS	62.27	CRIS: CLIP-Driven Referring Image Segmentation

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Referring Expression Segmentation On Refcoco 3

Metrics

Results