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 526–550 of 571 papers

TitleStatusHype
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer—0
Learning to Ground VLMs without Forgetting—0
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers—0
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision—0
Learning Visual Grounding from Generative Vision and Language Model—0
Learning with Difference Attention for Visually Grounded Self-supervised Representations—0
How direct is the link between words and images?—0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
Visual Grounding of Inter-lingual Word-Embeddings—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
Leveraging Past References for Robust Language Grounding—0
A survey on knowledge-enhanced multimodal learning—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
LidaRefer: Outdoor 3D Visual Grounding for Autonomous Driving with Transformers—0
Lightweight In-Context Tuning for Multimodal Unified Models—0
Like a bilingual baby: The advantage of visually grounding a bilingual language model—0
Language learning using Speech to Image retrieval—0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding—0
Knowledge Supports Visual Language Grounding: A Case Study on Colour Terms—0
Joint Top-Down and Bottom-Up Frameworks for 3D Visual Grounding—0
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—0
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter—0
LQMFormer: Language-aware Query Mask Transformer for Referring Image 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