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 351–400 of 571 papers

TitleStatusHype
From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
Parallel Vertex Diffusion for Unified Visual Grounding—0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding—0
Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning—0
Visual Prompting in Multimodal Large Language Models: A Survey—0
Context-Aware Indoor Point Cloud Object Generation through User Instructions—0
Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models—0
Focusing On Targets For Improving Weakly Supervised Visual Grounding—0
Programming with Pixels: Computer-Use Meets Software Engineering—0
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts—0
Visual Reference Resolution using Attention Memory for Visual Dialog—0
Propagating Over Phrase Relations for One-Stage Visual Grounding—0
ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding—0
ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning—0
FindIt: Generalized Localization with Natural Language Queries—0
Redemption Score: An Evaluation Framework to Rank Image Captions While Redeeming Image Semantics and Language Pragmatics—0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos—0
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations—0
Referencing Where to Focus: Improving VisualGrounding with Referential Query—0
Few-Shot Visual Grounding for Natural Human-Robot Interaction—0
Joint Visual Grounding with Language Scene Graphs—0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Referring to Screen Texts with Voice Assistants—0
FACET: Fairness in Computer Vision Evaluation Benchmark—0
Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog—0
Explainable Video Entailment With Grounded Visual Evidence—0
Learning to Assemble Neural Module Tree Networks for Visual Grounding—0
AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training—0
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models—0
Revisiting Data Auditing in Large Vision-Language Models—0
Revisiting Visual Grounding—0
AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions—0
Right Place, Right Time! Dynamizing Topological Graphs for Embodied Navigation—0
Extending CLIP's Image-Text Alignment to Referring Image Segmentation—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
RoViST: Learning Robust Metrics for Visual Storytelling—0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
VLMAE: Vision-Language Masked Autoencoder—0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought—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