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Referring Expression Segmentation On Refcoco 4

Metrics

Overall IoU

Results

Performance results of various models on this benchmark

Paper Title
HyperSeg83.5HyperSeg: Towards Universal Visual Segmentation with Large Language Model
MLCD-Seg-7B82.9Multi-label Cluster Discrimination for Visual Representation Learning
DETRIS78.6Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
EVF-SAM78.3EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
UniLSeg-10078.29Universal Segmentation at Arbitrary Granularity with Language Instruction
C3VG77.96Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
UniLSeg-2077.02Universal Segmentation at Arbitrary Granularity with Language Instruction
UNINEXT-H76.42Universal Instance Perception as Object Discovery and Retrieval
MaskRIS (Swin-B, combined DB)75.15MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
GROUNDHOG75.0GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
PolyFormer-L74.56PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
SafaRi-B74.53SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation
MaskRIS (Swin-B)74.46MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
PolyFormer-B72.89PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
MagNet71.32Mask Grounding for Referring Image Segmentation
ReLA71.02GRES: Generalized Referring Expression Segmentation
VLT68.43VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
LAVT68.38LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
CRIS68.08CRIS: CLIP-Driven Referring Image Segmentation
MaIL65.92MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation
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