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

Showing 501–550 of 571 papers

TitleStatusHype
Answer Questions with Right Image Regions: A Visual Attention Regularization ApproachCode0
Transformers in Vision: A Survey—0
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds—0
Explainable Video Entailment With Grounded Visual Evidence—0
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations—0
Class-agnostic Object Detection—0
Learning to ground medical text in a 3D human atlasCode0
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved ConsistencyCode0
Neural Twins TalkCode0
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary—0
Cosine meets Softmax: A tough-to-beat baseline for visual groundingCode0
AttnGrounder: Talking to Cars with AttentionCode0
Propagating Over Phrase Relations for One-Stage Visual Grounding—0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
Multi-Granularity Modularized Network for Abstract Visual Reasoning—0
Knowledge Supports Visual Language Grounding: A Case Study on Colour Terms—0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Visual Grounding Annotation of Recipe Flow Graph—0
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation—0
Giving Commands to a Self-driving Car: A Multimodal Reasoner for Visual Grounding—0
Emergent Communication with World Models—0
Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog—0
Connecting Vision and Language with Localized NarrativesCode0
Compositional Temporal Visual Grounding of Natural Language Event Descriptions—0
OptiBox: Breaking the Limits of Proposals for Visual Grounding—0
Leveraging Past References for Robust Language Grounding—0
Countering Language Drift via Visual Grounding—0
Language learning using Speech to Image retrieval—0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Multimodal Unified Attention Networks for Vision-and-Language Interactions—0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Transfer Learning from Audio-Visual Grounding to Speech Recognition—0
Joint Visual Grounding with Language Scene Graphs—0
Visually Grounded Neural Syntax Acquisition—0
Learning to Compose and Reason with Language Tree Structures for Visual Grounding—0
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval—0
Semantic query-by-example speech search using visual groundingCode0
Modularized Textual Grounding for Counterfactual ResilienceCode0
VQD: Visual Query Detection in Natural Scenes—0
Revisiting Visual Grounding—0
Learning semantic sentence representations from visually grounded language without lexical knowledgeCode0
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment—0
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
You Only Look & Listen Once: Towards Fast and Accurate Visual GroundingCode0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
Learning to Assemble Neural Module Tree Networks for Visual Grounding—0
Multi-task Learning of Hierarchical Vision-Language Representation—0
Being data-driven is not enough: Revisiting interactive instruction giving as a challenge for NLG—0
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization—0
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhatCode0
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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