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

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Showing 51–100 of 571 papers

TitleStatusHype
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AICode2
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator TrajectoriesCode2
DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real WorldCode2
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator TrajectoriesCode2
Reasoning to Attend: Try to Understand How <SEG> Token WorksCode2
VHM: Versatile and Honest Vision Language Model for Remote Sensing Image AnalysisCode2
Aligning and Prompting Everything All at Once for Universal Visual PerceptionCode2
Referring Image MattingCode2
LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an AgentCode2
SegVG: Transferring Object Bounding Box to Segmentation for Visual GroundingCode2
NExT-Chat: An LMM for Chat, Detection and SegmentationCode2
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMsCode2
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local AttentionCode2
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text PromptsCode2
ChatterBox: Multi-round Multimodal Referring and GroundingCode2
MedPromptX: Grounded Multimodal Prompting for Chest X-ray DiagnosisCode2
GTA1: GUI Test-time Scaling AgentCode2
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual GroundingCode2
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and LanguageCode2
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language ModelsCode1
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference ResolutionCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!Code1
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity RecognitionCode1
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human IntentionsCode1
Lexicon-Level Contrastive Visual-Grounding Improves Language ModelingCode1
Local-Global Context Aware Transformer for Language-Guided Video SegmentationCode1
Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in ClutterCode1
Learning Cross-modal Context Graph for Visual GroundingCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
Kosmos-2: Grounding Multimodal Large Language Models to the WorldCode1
Learning Cross-modal Context Graph for Visual GroundingCode1
Fine-Grained Semantically Aligned Vision-Language Pre-TrainingCode1
Iterative Robust Visual Grounding with Masked Reference based Centerpoint SupervisionCode1
An Efficient and Effective Transformer Decoder-Based Framework for Multi-Task Visual GroundingCode1
Cyclic Co-Learning of Sounding Object Visual Grounding and Sound SeparationCode1
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous DrivingCode1
Joint Visual Grounding and Tracking with Natural Language SpecificationCode1
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual GroundingCode1
CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding EvaluationCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
A Unified Framework for 3D Point Cloud Visual GroundingCode1
Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D ScansCode1
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language ModelsCode1
A Fast and Accurate One-Stage Approach to Visual GroundingCode1
Evolving Symbolic 3D Visual Grounder with Weakly Supervised ReflectionCode1
EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual GroundingCode1
CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual GroundingCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
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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