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 301–350 of 571 papers

TitleStatusHype
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and FilteringCode0
You Only Look & Listen Once: Towards Fast and Accurate Visual GroundingCode0
MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs—0
Visual Intention Grounding for Egocentric Assistants—0
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation—0
More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models—0
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level—0
Movie Box Office Prediction With Self-Supervised and Visually Grounded Pretraining—0
Image Difference Grounding with Natural Language—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
Visually grounded cross-lingual keyword spotting in speech—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation—0
Multi-Granularity Modularized Network for Abstract Visual Reasoning—0
Visually Grounded Neural Syntax Acquisition—0
Guiding Visual Question Answering with Attention Priors—0
GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents—0
Multimodal Reference Visual Grounding—0
Multimodal Unified Attention Networks for Vision-and-Language Interactions—0
Multi-task Learning of Hierarchical Vision-Language Representation—0
GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance—0
GroundFlow: A Plug-in Module for Temporal Reasoning on 3D Point Cloud Sequential Grounding—0
NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar—0
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners—0
GroundCap: A Visually Grounded Image Captioning Dataset—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding—0
Giving Commands to a Self-driving Car: A Multimodal Reasoner for Visual Grounding—0
A Neural Representation Framework with LLM-Driven Spatial Reasoning for Open-Vocabulary 3D Visual Grounding—0
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment—0
NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
OG: Equip vision occupancy with instance segmentation and visual grounding—0
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web—0
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval—0
On the Role of Visual Grounding in VQA—0
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting—0
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models—0
OptiBox: Breaking the Limits of Proposals for Visual Grounding—0
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks—0
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization—0
G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding—0
Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding—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