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Referring Expression Segmentation On Refcoco 5

Metrics

Overall IoU

Results

Performance results of various models on this benchmark

Paper Title
MLCD-Seg-7B75.6Multi-label Cluster Discrimination for Visual Representation Learning
HyperSeg75.2HyperSeg: Towards Universal Visual Segmentation with Large Language Model
DETRIS70.2Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
EVF-SAM70.1EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
C3VG68.95Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
UniLSeg-10068.15Universal Segmentation at Arbitrary Granularity with Language Instruction
UniLSeg-2066.99Universal Segmentation at Arbitrary Granularity with Language Instruction
UNINEXT-H66.22Universal Instance Perception as Object Discovery and Retrieval
GROUNDHOG64.9GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
SafaRi-B64.88SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation
MaskRIS (Swin-B, combined DB)62.83MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
PolyFormer-L61.87PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
MaskRIS (Swin-B)59.39MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
PolyFormer-B59.33PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
MagNet58.14Mask Grounding for Referring Image Segmentation
ReLA57.65GRES: Generalized Referring Expression Segmentation
VLT56.92VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
MaIL56.06MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation
LAVT55.1LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
CRIS53.68CRIS: CLIP-Driven Referring Image Segmentation
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