SOTAVerified

Visual Grounding

Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. The query can be a phrase, a sentence, or even a multi-round dialogue. There are three main challenges in VG:

  • What is the main focus in a query?
  • How to understand an image?
  • How to locate an object?

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 571 papers

TitleStatusHype
Towards Visual Text Grounding of Multimodal Large Language Model—0
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target GranularitiesCode0
Image Difference Grounding with Natural Language—0
Multimodal Reference Visual Grounding—0
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote SensingCode0
ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning—0
Efficient Adaptation For Remote Sensing Visual Grounding—0
NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving—0
Beyond Object Categories: Multi-Attribute Reference Understanding for Visual Grounding—0
Seeing Speech and Sound: Distinguishing and Locating Audios in Visual Scenes—0
A Vision Centric Remote Sensing Benchmark—0
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data GenerationCode0
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding—0
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions—0
Teaching Metric Distance to Autoregressive Multimodal Foundational Models—0
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning—0
ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding—0
Programming with Pixels: Computer-Use Meets Software Engineering—0
GroundCap: A Visually Grounded Image Captioning Dataset—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language Navigation—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring—0
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models—0
ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding—0
Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes—0
Ges3ViG : Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference UnderstandingCode0
VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Task-aware Cross-modal Feature Refinement Transformer with Large Language Models for Visual Grounding—0
Referencing Where to Focus: Improving VisualGrounding with Referential Query—0
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language ModelsCode0
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues—0
FiVL: A Framework for Improved Vision-Language AlignmentCode0
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting—0
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language ModelsCode0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
M^3D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information ExtractionCode0
SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding—0
Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding—0
3D Scene Graph Guided Vision-Language Pre-training—0
Visual Contexts Clarify Ambiguous Expressions: A Benchmark DatasetCode0
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level—0
LidaRefer: Outdoor 3D Visual Grounding for Autonomous Driving with Transformers—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
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All datasetsRefCOCO testARefCOCO+ test BRefCoCo val

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Florence-2-large-ftAccuracy (%)95.3—Unverified
2mPLUG-2Accuracy (%)92.8—Unverified
3X2-VLM (large)Accuracy (%)92.1—Unverified
4XFM (base)Accuracy (%)90.4—Unverified
5X2-VLM (base)Accuracy (%)90.3—Unverified
6X-VLM (base)Accuracy (%)89—Unverified
7HYDRAIoU61.7—Unverified
8HYDRAIoU61.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Florence-2-large-ftAccuracy (%)92—Unverified
2mPLUG-2Accuracy (%)86.05—Unverified
3X2-VLM (large)Accuracy (%)81.8—Unverified
4XFM (base)Accuracy (%)79.8—Unverified
5X2-VLM (base)Accuracy (%)78.4—Unverified
6X-VLM (base)Accuracy (%)76.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Florence-2-large-ftAccuracy (%)93.4—Unverified
2mPLUG-2Accuracy (%)90.33—Unverified
3X2-VLM (large)Accuracy (%)87.6—Unverified
4XFM (base)Accuracy (%)86.1—Unverified
5X2-VLM (base)Accuracy (%)85.2—Unverified
6X-VLM (base)Accuracy (%)84.51—Unverified