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Generalized Referring Expression Segmentation

Generalized Referring Expression Segmentation (GRES), introduced by Liu et al in CVPR 2023, allows expressions indicating any number of target objects. GRES takes an image and a referring expression as input, and requires mask prediction of the target object(s).

Papers

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Showing 1–15 of 15 papers

TitleStatusHype
DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback SynergyCode1
Refer to Anything with Vision-Language Prompts—0
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension—0
Instance-Aware Generalized Referring Expression Segmentation—0
Bring Adaptive Binding Prototypes to Generalized Referring Expression SegmentationCode0
CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression SegmentationCode1
PSALM: Pixelwise SegmentAtion with Large Multi-Modal ModelCode3
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation—0
GSVA: Generalized Segmentation via Multimodal Large Language ModelsCode1
GRES: Generalized Referring Expression SegmentationCode2
LAVT: Language-Aware Vision Transformer for Referring Image SegmentationCode1
CRIS: CLIP-Driven Referring Image SegmentationCode1
Vision-Language Transformer and Query Generation for Referring SegmentationCode1
Locate then Segment: A Strong Pipeline for Referring Image Segmentation—0
MAttNet: Modular Attention Network for Referring Expression ComprehensionCode0
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