RelCheck: Dual-Evidence Spatial Grounding for VLM Hallucination Correction

arXiv:2609.27890 · cs.CV, cs.AI · Submitted 2026-08-21 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-21

Updated: 2026-08-21

Terminology

• Hallucination — 48× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Grounding — 15× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Visual Grounding — 4× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Quantization — 1× in this paper · explained in 12 episodes

Sources

• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
• Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models