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 401–425 of 571 papers

TitleStatusHype
Semantic sentence similarity: size does not always matter—0
Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution—0
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation—0
SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding—0
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning—0
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery—0
Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding—0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding—0
Task-aware Cross-modal Feature Refinement Transformer with Large Language Models for Visual Grounding—0
Task-oriented Sequential Grounding in 3D Scenes—0
Teaching Metric Distance to Autoregressive Multimodal Foundational Models—0
Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding—0
The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA—0
Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases—0
Towards Open-World Grasping with Large Vision-Language Models—0
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers—0
Towards Visual Text Grounding of Multimodal Large Language Model—0
Training-Free Reasoning and Reflection in MLLMs—0
Transfer Learning from Audio-Visual Grounding to Speech Recognition—0
Transformers in Vision: A Survey—0
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding—0
TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language Navigation—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