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 376–400 of 571 papers

TitleStatusHype
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