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 501–550 of 571 papers

TitleStatusHype
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference UnderstandingCode0
You Only Look & Listen Once: Towards Fast and Accurate Visual GroundingCode0
AS3D: 2D-Assisted Cross-Modal Understanding with Semantic-Spatial Scene Graphs for 3D Visual GroundingCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph AttentionCode0
Finding beans in burgers: Deep semantic-visual embedding with localizationCode0
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Multi-Attribute Interactions Matter for 3D Visual GroundingCode0
Unveiling the Compositional Ability Gap in Vision-Language Reasoning ModelCode0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
Exploring Phrase-Level Grounding with Text-to-Image Diffusion ModelCode0
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and FilteringCode0
Modularized Textual Grounding for Counterfactual ResilienceCode0
Mismatch Quest: Visual and Textual Feedback for Image-Text MisalignmentCode0
Measuring Faithful and Plausible Visual Grounding in VQACode0
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMsCode0
Self-view Grounding Given a Narrated 360° VideoCode0
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
Semantic query-by-example speech search using visual groundingCode0
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote SensingCode0
M^3D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information ExtractionCode0
Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UICode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word RepresentationsCode0
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data GenerationCode0
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question AnsweringCode0
Learning Two-Branch Neural Networks for Image-Text Matching TasksCode0
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual GroundingCode0
Learning to ground medical text in a 3D human atlasCode0
Smart Vision-Language ReasonersCode0
Learning semantic sentence representations from visually grounded language without lexical knowledgeCode0
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved ConsistencyCode0
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic RepresentationCode0
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using DeterminersCode0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Language with Vision: a Study on Grounded Word and Sentence EmbeddingsCode0
Adaptive Masking Enhances Visual GroundingCode0
Deconfounded Visual GroundingCode0
Visually Grounded VQA by Lattice-based RetrievalCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Language Adaptive Weight Generation for Multi-task Visual GroundingCode0
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhatCode0
Collecting Visually-Grounded Dialogue with A Game Of SortsCode0
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot InteractionsCode0
CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-raysCode0
Investigating Compositional Challenges in Vision-Language Models for Visual GroundingCode0
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language ModelsCode0
HumaniBench: A Human-Centric Framework for Large Multimodal Models EvaluationCode0
Answer Questions with Right Image Regions: A Visual Attention Regularization ApproachCode0
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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