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 326–350 of 571 papers

TitleStatusHype
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