PND reduces object hallucination in VLMs via a dual-path contrast during decoding that amplifies visual features and penalizes linguistic priors, achieving reported SOTA results on POPE, MME, and CHAIR without retraining.
Large vision-language model alignment and misalignment: A survey through the lens of explainability
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 3polarities
background 3representative citing papers
OPPO is an evidence-aware preference optimization objective that contrasts faithful responses under varying visual evidence strengths to reduce hallucinations in MLLMs.
LMM-Searcher uses file-based visual UIDs and a fetch tool plus 12K synthesized trajectories to fine-tune a multimodal agent that scales to 100-turn horizons and reaches SOTA among open-source models on MM-BrowseComp and MMSearch-Plus.
State-of-the-art MLLMs show substantial inconsistency when reasoning over the same information presented in image, text, or mixed modalities, even after accounting for OCR errors, with inconsistency linked to visual factors and modality gap.
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
A survey reviewing the integration of generative models with connected and automated vehicles to enhance predictive modeling, simulation accuracy, and decision-making.
citing papers explorer
-
Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI
A survey reviewing the integration of generative models with connected and automated vehicles to enhance predictive modeling, simulation accuracy, and decision-making.