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

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Showing 201–250 of 571 papers

TitleStatusHype
TubeDETR: Spatio-Temporal Video Grounding with TransformersCode1
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and MemoryCode1
Visual Position Prompt for MLLM based Visual GroundingCode1
Text-to-Image Generation Grounded by Fine-Grained User AttentionCode1
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic RepresentationCode0
Dual Attention Networks for Visual Reference Resolution in Visual DialogCode0
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved ConsistencyCode0
Language-Guided Diffusion Model for Visual GroundingCode0
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
Language Adaptive Weight Generation for Multi-task Visual GroundingCode0
Smart Vision-Language ReasonersCode0
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhatCode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot InteractionsCode0
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual GroundingCode0
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question AnsweringCode0
Self-view Grounding Given a Narrated 360° VideoCode0
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph AttentionCode0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Semantic query-by-example speech search using visual groundingCode0
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using DeterminersCode0
RoViST:Learning Robust Metrics for Visual StorytellingCode0
A Better Loss for Visual-Textual GroundingCode0
RoViST: Learning Robust Metrics for Visual StorytellingCode0
Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language ModelsCode0
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based LocalizationCode0
Rethinking Diversified and Discriminative Proposal Generation for Visual GroundingCode0
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic ApproachCode0
Investigating Compositional Challenges in Vision-Language Models for Visual GroundingCode0
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingCode0
Revisiting Visual Question Answering BaselinesCode0
HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasksCode0
Deconfounded Visual GroundingCode0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language ModelsCode0
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic ManipulationCode0
CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-raysCode0
Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction for Visual GroundingCode0
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference UnderstandingCode0
To Find Waldo You Need Contextual Cues: Debiasing Who's WaldoCode0
Grounding of Textual Phrases in Images by ReconstructionCode0
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and RepetitionCode0
GROOViST: A Metric for Grounding Objects in Visual StorytellingCode0
Neural Twins TalkCode0
Language with Vision: a Study on Grounded Word and Sentence EmbeddingsCode0
AttnGrounder: Talking to Cars with AttentionCode0
Ges3ViG : Incorporating Pointing Gestures into Language-Based 3D Visual Grounding for Embodied Reference UnderstandingCode0
Cost-Effective Language Driven Image Editing with LX-DRIMCode0
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative LearningCode0
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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