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
Cost-Effective Language Driven Image Editing with LX-DRIMCode0
Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding—0
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
Visual Grounding of Inter-lingual Word-Embeddings—0
VLMAE: Vision-Language Masked Autoencoder—0
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual GroundingCode0
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases—0
RoViST: Learning Robust Metrics for Visual StorytellingCode0
How direct is the link between words and images?—0
Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Grounding—0
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution—0
Language with Vision: a Study on Grounded Word and Sentence EmbeddingsCode0
Guiding Visual Question Answering with Attention Priors—0
Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution—0
Weakly-supervised segmentation of referring expressions—0
RoViST:Learning Robust Metrics for Visual StorytellingCode0
Flexible Visual GroundingCode0
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision TransformerCode0
To Find Waldo You Need Contextual Cues: Debiasing Who’s WaldoCode0
FindIt: Generalized Localization with Natural Language Queries—0
To Find Waldo You Need Contextual Cues: Debiasing Who's WaldoCode0
Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding—0
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge—0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds—0
Deconfounded Visual GroundingCode0
RoViST: Learning Robust Metrics for Visual Storytelling—0
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding—0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
Zero-Shot Visual Grounding of Referring Utterances in Dialogue—0
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer—0
Efficient Multi-Modal Embeddings from Structured Data—0
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question AnsweringCode0
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models—0
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter—0
A Better Loss for Visual-Textual GroundingCode0
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding—0
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers—0
Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs—0
AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training—0
Attention-Based Keyword Localisation in Speech using Visual Grounding—0
Semantic sentence similarity: size does not always matter—0
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic RepresentationCode0
Visual Grounding Strategies for Text-Only Natural Language Processing—0
Scene-Intuitive Agent for Remote Embodied Visual Grounding—0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
Few-Shot Visual Grounding for Natural Human-Robot Interaction—0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
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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