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
Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding—0
Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction for Visual GroundingCode0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Joint Top-Down and Bottom-Up Frameworks for 3D Visual Grounding—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
Context-Infused Visual Grounding for ArtCode0
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMsCode0
Learning to Ground VLMs without Forgetting—0
Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics—0
GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance—0
Context-Aware Command Understanding for Tabletop Scenarios—0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
Adaptive Masking Enhances Visual GroundingCode0
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and FilteringCode0
Individuation in Neural Models with and without Visual Grounding—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
Bayesian Self-Training for Semi-Supervised 3D Segmentation—0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Visual Prompting in Multimodal Large Language Models: A Survey—0
NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar—0
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingCode0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
MMR: Evaluating Reading Ability of Large Multimodal Models—0
Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models—0
Task-oriented Sequential Grounding in 3D Scenes—0
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models—0
Unveiling and Mitigating Bias in Audio Visual Segmentation—0
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding—0
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models—0
Learning Visual Grounding from Generative Vision and Language Model—0
VIMI: Grounding Video Generation through Multi-modal Instruction—0
Exploring Phrase-Level Grounding with Text-to-Image Diffusion ModelCode0
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and RepetitionCode0
Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge—0
Smart Vision-Language ReasonersCode0
ACTRESS: Active Retraining for Semi-supervised Visual Grounding—0
Visual Grounding with Attention-Driven Constraint Balancing—0
The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA—0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts—0
Towards Open-World Grasping with Large Vision-Language Models—0
On the Role of Visual Grounding in VQA—0
Visually Consistent Hierarchical Image Classification—0
Learning Language Structures through Grounding—0
Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding—0
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—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