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 401–450 of 571 papers

TitleStatusHype
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative LearningCode0
Lightweight In-Context Tuning for Multimodal Unified Models—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
Collecting Visually-Grounded Dialogue with A Game Of SortsCode0
Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding—0
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using DeterminersCode0
Interpretable Visual Question Answering via Reasoning Supervision—0
FACET: Fairness in Computer Vision Evaluation Benchmark—0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasksCode0
Language-Guided Diffusion Model for Visual GroundingCode0
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding—0
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic ManipulationCode0
OG: Equip vision occupancy with instance segmentation and visual grounding—0
Learning with Difference Attention for Visually Grounded Self-supervised Representations—0
Extending CLIP's Image-Text Alignment to Referring Image Segmentation—0
Referring to Screen Texts with Voice Assistants—0
Language Adaptive Weight Generation for Multi-task Visual GroundingCode0
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word RepresentationsCode0
Benchmarking Diverse-Modal Entity Linking with Generative Models—0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
Measuring Faithful and Plausible Visual Grounding in VQACode0
An Examination of the Robustness of Reference-Free Image Captioning Evaluation MetricsCode0
TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Sample-Specific Debiasing for Better Image-Text Models—0
Movie Box Office Prediction With Self-Supervised and Visually Grounded Pretraining—0
WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural LanguageCode0
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference UnderstandingCode0
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment—0
Parallel Vertex Diffusion for Unified Visual Grounding—0
Focusing On Targets For Improving Weakly Supervised Visual Grounding—0
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding TasksCode0
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding—0
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition—0
Dynamic Inference With Grounding Based Vision and Language Models—0
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks—0
Using Multiple Instance Learning to Build Multimodal Representations—0
UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding—0
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding—0
A survey on knowledge-enhanced multimodal learning—0
Visually Grounded VQA by Lattice-based RetrievalCode0
Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue?—0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
Like a bilingual baby: The advantage of visually grounding a bilingual language model—0
YFACC: A Yorùbá speech-image dataset for cross-lingual keyword localisation through visual grounding—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—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