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 451–500 of 571 papers

TitleStatusHype
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
D2AF: A Dual-Driven Annotation and Filtering Framework for Visual Grounding—0
Cycle-Consistency Learning for Captioning and Grounding—0
COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts—0
Countering Language Drift via Visual 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
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
3DWG: 3D Weakly Supervised Visual Grounding via Category and Instance-Level Alignment—0
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases—0
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach—0
Towards Open-World Grasping with Large Vision-Language Models—0
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers—0
Zero-Shot 3D Visual Grounding from Vision-Language Models—0
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition—0
Context-Aware Command Understanding for Tabletop Scenarios—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
Compositional Temporal Visual Grounding of Natural Language Event Descriptions—0
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary—0
TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language Navigation—0
TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding—0
Class-agnostic Object Detection—0
Two Causally Related Needles in a Video Haystack—0
Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding—0
Uni3DL: Unified Model for 3D and Language Understanding—0
Unified Representation Space for 3D Visual Grounding—0
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds—0
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations—0
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds—0
Bridging Modality Gap for Visual Grounding with Effecitve Cross-modal Distillation—0
UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding—0
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
Unveiling and Mitigating Bias in Audio Visual Segmentation—0
BlenderAlchemy: Editing 3D Graphics with Vision-Language Models—0
Beyond Object Categories: Multi-Attribute Reference Understanding for Visual Grounding—0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—0
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models—0
Benchmarking Diverse-Modal Entity Linking with Generative Models—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Being data-driven is not enough: Revisiting interactive instruction giving as a challenge for NLG—0
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution—0
Bayesian Self-Training for Semi-Supervised 3D Segmentation—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