Cover Image

ZINA: Multimodal Fine-grained Hallucination Detection and Editing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wada, Yuiga, Matsuda, Kazuki, Sugiura, Komei, Neubig, Graham
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning
by: Matsuda, Kazuki, et al.
Published: (2024)

VELA: An LLM-Hybrid-as-a-Judge Approach for Evaluating Long Image Captions
by: Matsuda, Kazuki, et al.
Published: (2025)

Polos: Multimodal Metric Learning from Human Feedback for Image Captioning
by: Wada, Yuiga, et al.
Published: (2024)

LLM-Free Image Captioning Evaluation in Reference-Flexible Settings
by: Hirano, Shinnosuke, et al.
Published: (2025)

Capturing Fine-Grained Alignments Improves 3D Affordance Detection
by: Tokumitsu, Junsei, et al.
Published: (2025)

Attention Lattice Adapter: Visual Explanation Generation for Visual Foundation Model
by: Hirano, Shinnosuke, et al.
Published: (2025)

MLLM-as-a-Judge Exhibits Model Preference Bias
by: Koyama, Shuitsu, et al.
Published: (2026)

JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
by: Onohara, Shota, et al.
Published: (2024)

Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images
by: Nagashima, Shunya, et al.
Published: (2025)

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)

EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
by: Lu, Xingyuan, et al.
Published: (2025)

Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding
by: Suzuki, Shuntaro, et al.
Published: (2025)

Affordance RAG: Hierarchical Multimodal Retrieval with Affordance-Aware Embodied Memory for Mobile Manipulation
by: Korekata, Ryosuke, et al.
Published: (2025)

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding
by: Faure, Gueter Josmy, et al.
Published: (2026)

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
by: You, Liangliang, et al.
Published: (2025)

Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting
by: Wu, Jiarui, et al.
Published: (2025)

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
by: Zhong, Weihong, et al.
Published: (2024)

TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
by: Cai, Mu, et al.
Published: (2024)

Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense Labeling
by: Yashima, Daichi, et al.
Published: (2024)

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
by: Liu, Chengzhi, et al.
Published: (2025)

Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
by: Wang, Zehao, et al.
Published: (2024)

Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
by: Fu, Yuhan, et al.
Published: (2024)

DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
by: Lin, Haokun, et al.
Published: (2026)

FINER: MLLMs Hallucinate under Fine-grained Negative Queries
by: Xiao, Rui, et al.
Published: (2026)

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization
by: Wu, Jiulong, et al.
Published: (2025)

GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity
by: Park, Seongheon, et al.
Published: (2025)

Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
by: Xiao, Wenyi, et al.
Published: (2024)

Steering the Verifiability of Multimodal AI Hallucinations
by: Pang, Jianhong, et al.
Published: (2026)

Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach
by: Sakajo, Haruki, et al.
Published: (2025)

Motion Generation from Fine-grained Textual Descriptions
by: Li, Kunhang, et al.
Published: (2024)

HA-FGOVD: Highlighting Fine-grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection
by: Ma, Yuqi, et al.
Published: (2024)

VIMI: Grounding Video Generation through Multi-modal Instruction
by: Fang, Yuwei, et al.
Published: (2024)

DM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions
by: Korekata, Ryosuke, et al.
Published: (2024)

Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization
by: Compagnoni, Alberto, et al.
Published: (2025)

Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions
by: Fu, Yuhan, et al.
Published: (2024)

MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
by: Baek, Jeonghun, et al.
Published: (2025)

Woodpecker: Hallucination Correction for Multimodal Large Language Models
by: Yin, Shukang, et al.
Published: (2023)

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
by: Sun, Haoyuan, et al.
Published: (2025)

Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination
by: Bi, Qi, et al.
Published: (2025)

Efficient Model Editing with Task-Localized Sparse Fine-tuning
by: Iurada, Leonardo, et al.
Published: (2025)