Context-Aware Decoding for Faithful Vision-Language Generation

arXiv:2601.05939 · cs.CV · Submitted 2026-01-09 · Read on arXiv

cs.CV

Submitted: 2026-01-09

Updated: 2026-01-09

Terminology

• Hallucination — 5× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Context Embedding Injection — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Hallucination of Multimodal Large Language Models: A Survey
• Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
• DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
• Contrastive Decoding: Open-ended Text Generation as Optimization
• Evaluating Object Hallucination in Large Vision-Language Models
• Microsoft COCO: Common Objects in Context
• A Survey on Hallucination in Large Vision-Language Models
• Visual Instruction Tuning
• Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
• Learning Transferable Visual Models From Natural Language Supervision
• Aligning Large Multimodal Models with Factually Augmented RLHF
• Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
• LLaMA: Open and Efficient Foundation Language Models
• AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

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