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Referring Expression Segmentation
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The task aims at labeling the pixels of an image or video that represent an object instance referred by a linguistic expression. In particular, the referring expression (RE) must allow the identification of an individual object in a discourse or scene (the referent). REs unambiguously identify the target instance.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
Refer to Anything with Vision-Language Prompts
RemoteSAM: Towards Segment Anything for Earth Observation
VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning
RESAnything: Attribute Prompting for Arbitrary Referring Segmentation
3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations
MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
IPDN: Image-enhanced Prompt Decoding Network for 3D Referring Expression Segmentation
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
HyperSeg: Towards Universal Visual Segmentation with Large Language Model
Instance-Aware Generalized Referring Expression Segmentation
SegLLM: Multi-round Reasoning Segmentation
Text4Seg: Reimagining Image Segmentation as Text Generation
SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation
3D-GRES: Generalized 3D Referring Expression Segmentation
Multi-label Cluster Discrimination for Visual Representation Learning
ViLLa: Video Reasoning Segmentation with Large Language Model
SafaRi:Adaptive Sequence Transformer for Weakly Supervised Referring Expression Segmentation
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation
F-LMM: Grounding Frozen Large Multimodal Models
SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation
GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane
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