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 326–350 of 571 papers

TitleStatusHype
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