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 501–550 of 571 papers

TitleStatusHype
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
VidLA: Video-Language Alignment at Scale—0
Viewpoint-Aware Visual Grounding in 3D Scenes—0
A Vision Centric Remote Sensing Benchmark—0
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding—0
ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition—0
ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding—0
3D Scene Graph Guided Vision-Language Pre-training—0
YFACC: A Yorùbá speech-image dataset for cross-lingual keyword localisation through visual grounding—0
AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring—0
VIMI: Grounding Video Generation through Multi-modal Instruction—0
Attention-Based Keyword Localisation in Speech using Visual Grounding—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
VIST-GPT: Ushering in the Era of Visual Storytelling with LLMs?—0
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding—0
Zero-Shot Visual Grounding of Referring Utterances in Dialogue—0
Visual Grounding Annotation of Recipe Flow Graph—0
Learning from Synthetic Data for Visual Grounding—0
Visually Consistent Hierarchical Image Classification—0
Learning Language Structures through Grounding—0
Visual grounding for desktop graphical user interfaces—0
Learning to Compose and Reason with Language Tree Structures for Visual Grounding—0
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