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
Improved Visual Grounding through Self-Consistent Explanations—0
Improving Visually Grounded Sentence Representations with Self-Attention—0
Individuation in Neural Models with and without Visual Grounding—0
Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention—0
Interactive Reinforcement Learning for Object Grounding via Self-Talking—0
Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction—0
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining—0
Interpretable Visual Question Answering via Reasoning Supervision—0
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter—0
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—0
Joint Top-Down and Bottom-Up Frameworks for 3D Visual Grounding—0
Knowledge Supports Visual Language Grounding: A Case Study on Colour Terms—0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
Language learning using Speech to Image retrieval—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
Learning from Synthetic Data for Visual Grounding—0
Visually Consistent Hierarchical Image Classification—0
Learning Language Structures through Grounding—0
Learning to Compose and Reason with Language Tree Structures for Visual Grounding—0
Learning to Ground VLMs without Forgetting—0
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision—0
Learning Visual Grounding from Generative Vision and Language Model—0
Learning with Difference Attention for Visually Grounded Self-supervised Representations—0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
Leveraging Past References for Robust Language Grounding—0
LidaRefer: Outdoor 3D Visual Grounding for Autonomous Driving with Transformers—0
Lightweight In-Context Tuning for Multimodal Unified Models—0
Like a bilingual baby: The advantage of visually grounding a bilingual language model—0
LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding—0
LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment—0
MedRG: Medical Report Grounding with Multi-modal Large Language Model—0
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding—0
Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration—0
MMR: Evaluating Reading Ability of Large Multimodal Models—0
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding—0
MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs—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
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation—0
Multi-Granularity Modularized Network for Abstract Visual Reasoning—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
NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar—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