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
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
Don't Look Only Once: Towards Multimodal Interactive Reasoning with Selective Visual Revisitation—0
Data-Efficient 3D Visual Grounding via Order-Aware Referring—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding—0
Dynamic Inference With Grounding Based Vision and Language Models—0
Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding—0
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models—0
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues—0
EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments—0
Efficient Adaptation For Remote Sensing Visual Grounding—0
Efficient Multi-Modal Embeddings from Structured Data—0
Emergent Communication with World Models—0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Learning to Assemble Neural Module Tree Networks for Visual Grounding—0
Explainable Video Entailment With Grounded Visual Evidence—0
FACET: Fairness in Computer Vision Evaluation Benchmark—0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Few-Shot Visual Grounding for Natural Human-Robot Interaction—0
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos—0
FindIt: Generalized Localization with Natural Language Queries—0
Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts—0
Focusing On Targets For Improving Weakly Supervised Visual Grounding—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes—0
G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding—0
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks—0
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
Giving Commands to a Self-driving Car: A Multimodal Reasoner for Visual Grounding—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
GroundCap: A Visually Grounded Image Captioning Dataset—0
GroundFlow: A Plug-in Module for Temporal Reasoning on 3D Point Cloud Sequential Grounding—0
GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance—0
GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents—0
Guiding Visual Question Answering with Attention Priors—0
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
Image Difference Grounding with Natural Language—0
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation—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