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 401–450 of 571 papers

TitleStatusHype
Fine-Grained Semantically Aligned Vision-Language Pre-TrainingCode1
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
Improving Visual Grounding by Encouraging Consistent Gradient-based ExplanationsCode1
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
MixGen: A New Multi-Modal Data AugmentationCode1
TransVG++: End-to-End Visual Grounding with Language Conditioned Vision TransformerCode1
Referring Image MattingCode2
Guiding Visual Question Answering with Attention Priors—0
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
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
To Find Waldo You Need Contextual Cues: Debiasing Who’s WaldoCode0
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision TransformerCode0
Improving Visual Grounding with Visual-Linguistic Verification and Iterative ReasoningCode1
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive SelectionCode1
Multi-View Transformer for 3D Visual GroundingCode1
FindIt: Generalized Localization with Natural Language Queries—0
To Find Waldo You Need Contextual Cues: Debiasing Who's WaldoCode0
Collaborative Transformers for Grounded Situation RecognitionCode1
TubeDETR: Spatio-Temporal Video Grounding with TransformersCode1
SeqTR: A Simple yet Universal Network for Visual GroundingCode1
Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual GroundingCode1
Word Discovery in Visually Grounded, Self-Supervised Speech ModelsCode1
Local-Global Context Aware Transformer for Language-Guided Video SegmentationCode1
Pseudo-Q: Generating Pseudo Language Queries for Visual GroundingCode1
REX: Reasoning-aware and Grounded ExplanationCode1
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
Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language ModelingCode1
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Multi-Modal Dynamic Graph Transformer for Visual GroundingCode1
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
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding—0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Grounded Situation Recognition with TransformersCode1
Zero-Shot Visual Grounding of Referring Utterances in Dialogue—0
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual ConceptsCode1
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
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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