Visual Grounding
4개 벤치마크 · 논문 1,131편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
Towards Visual Grounding: A Survey
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
Papers
VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction
Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors often limits their ability to bridge unobs…
Reinforcement LearningVisual GroundingCoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement
Visual grounding localizes an object described by language with a bounding box. Most multimodal grounding models compress target identification, spatial reasoning, and boundary estimation into one terminal prediction. Fr…
Spatial ReasoningVisual GroundingMotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefit…
Zero-shot GeneralizationRobot ManipulationVisual GroundingBeyond Selection: Token Parameterization for Extreme Visual Token Compression
Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can break visual grounding while learned resamplers increase parameter co…
Visual GroundingViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models
Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation methods rely on training-phase adjustments or post-hoc calibration, b…
Visual GroundingImage CaptioningText GenerationWhat Do Hallucinations Reveal About Multimodal Reasoning? Diagnosing Visual Grounding Failures via Contrastive Decoding Probes
When strong multimodal models are widely available, progress requires new scientific methodologies beyond benchmark scores---using models as instruments for understanding behavior. We address this by asking: can we use l…
Multimodal ReasoningVisual Grounding