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 251–275 of 571 papers

TitleStatusHype
DenseGrounding: Improving Dense Language-Vision Semantics for Ego-Centric 3D Visual Grounding—0
3DWG: 3D Weakly Supervised Visual Grounding via Category and Instance-Level Alignment—0
Learning Language Structures through Grounding—0
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models—0
Improved Visual Grounding through Self-Consistent Explanations—0
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation—0
Image Difference Grounding with Natural Language—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution—0
3D Scene Graph Guided Vision-Language Pre-training—0
Multimodal Reference Visual Grounding—0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
Bayesian Self-Training for Semi-Supervised 3D Segmentation—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding—0
Multi-Granularity Modularized Network for Abstract Visual Reasoning—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
D2AF: A Dual-Driven Annotation and Filtering Framework for Visual Grounding—0
HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation—0
Cycle-Consistency Learning for Captioning and Grounding—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
Guiding Visual Question Answering with Attention Priors—0
A Vision Centric Remote Sensing Benchmark—0
Multimodal Unified Attention Networks for Vision-and-Language Interactions—0
Show:102550
← PrevPage 11 of 23Next →
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