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 351–400 of 571 papers

TitleStatusHype
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners—0
Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
OG: Equip vision occupancy with instance segmentation and visual grounding—0
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web—0
Omni-Q: Omni-Directional Scene Understanding for Unsupervised Visual Grounding—0
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval—0
On the Role of Visual Grounding in VQA—0
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models—0
OptiBox: Breaking the Limits of Proposals for Visual Grounding—0
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization—0
Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding—0
Parallel Vertex Diffusion for Unified Visual Grounding—0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding—0
Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning—0
Context-Aware Indoor Point Cloud Object Generation through User Instructions—0
Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models—0
Programming with Pixels: Computer-Use Meets Software Engineering—0
Propagating Over Phrase Relations for One-Stage Visual Grounding—0
ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding—0
ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning—0
Redemption Score: An Evaluation Framework to Rank Image Captions While Redeeming Image Semantics and Language Pragmatics—0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations—0
Referencing Where to Focus: Improving VisualGrounding with Referential Query—0
Joint Visual Grounding with Language Scene Graphs—0
Referring to Screen Texts with Voice Assistants—0
Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models—0
Revisiting Data Auditing in Large Vision-Language Models—0
Revisiting Visual Grounding—0
Right Place, Right Time! Dynamizing Topological Graphs for Embodied Navigation—0
Extending CLIP's Image-Text Alignment to Referring Image Segmentation—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
RoViST: Learning Robust Metrics for Visual Storytelling—0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought—0
Sample-Specific Debiasing for Better Image-Text Models—0
Scene-Intuitive Agent for Remote Embodied Visual Grounding—0
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding—0
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling—0
Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge—0
SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding—0
Seeing Speech and Sound: Distinguishing and Locating Audios in Visual Scenes—0
Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes—0
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge—0
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding—0
Semantic Localization Guiding Segment Anything Model For Reference Remote Sensing 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