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 351–400 of 571 papers

TitleStatusHype
Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention—0
LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding—0
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding—0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Visual grounding for desktop graphical user interfaces—0
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners—0
BlenderAlchemy: Editing 3D Graphics with Vision-Language Models—0
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based LocalizationCode0
MedRG: Medical Report Grounding with Multi-modal Large Language Model—0
Data-Efficient 3D Visual Grounding via Order-Aware Referring—0
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
VidLA: Video-Language Alignment at Scale—0
Learning from Synthetic Data for Visual Grounding—0
WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radar—0
Right Place, Right Time! Dynamizing Topological Graphs for Embodied Navigation—0
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph AttentionCode0
Detecting Concrete Visual Tokens for Multimodal Machine Translation—0
Adversarial Testing for Visual Grounding via Image-Aware Property Reduction—0
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web—0
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward ModelingCode0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding—0
Uncovering the Full Potential of Visual Grounding Methods in VQACode0
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers—0
LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation—0
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach—0
Viewpoint-Aware Visual Grounding in 3D Scenes—0
Investigating Compositional Challenges in Vision-Language Models for Visual GroundingCode0
Multi-Attribute Interactions Matter for 3D Visual GroundingCode0
Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and ConsistencyCode0
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—0
G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding—0
Bridging Modality Gap for Visual Grounding with Effecitve Cross-modal Distillation—0
Cycle-Consistency Learning for Captioning and Grounding—0
Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment—0
Visual Grounding of Whole Radiology Reports for 3D CT Images—0
Improved Visual Grounding through Self-Consistent Explanations—0
Mismatch Quest: Visual and Textual Feedback for Image-Text MisalignmentCode0
Uni3DL: Unified Model for 3D and Language Understanding—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-trainingCode0
Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language ModelsCode0
Context-Aware Indoor Point Cloud Object Generation through User Instructions—0
Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language ModelsCode0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
GROOViST: A Metric for Grounding Objects in Visual StorytellingCode0
Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching NetworkCode0
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot InteractionsCode0
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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