Copertina

H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pham, Nhi, Schott, Michael
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
di: Chen, Jiawei, et al.
Pubblicazione: (2024)

Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models
di: Wu, Mingrui, et al.
Pubblicazione: (2024)

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
di: Jing, Liqiang, et al.
Pubblicazione: (2023)

Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
di: Nguyen, Minh Khoi, et al.
Pubblicazione: (2026)

Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
di: Lyu, Xinyu, et al.
Pubblicazione: (2024)

RePOPE: Impact of Annotation Errors on the POPE Benchmark
di: Neuhaus, Yannic, et al.
Pubblicazione: (2025)

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations
di: Lu, Yifan, et al.
Pubblicazione: (2025)

Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models
di: Seth, Ashish, et al.
Pubblicazione: (2024)

Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models
di: Sun, Yaqi, et al.
Pubblicazione: (2025)

Personalized Large Vision-Language Models
di: Pham, Chau, et al.
Pubblicazione: (2024)

Lyapunov Probes for Hallucination Detection in Large Foundation Models
di: Luan, Bozhi, et al.
Pubblicazione: (2026)

Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models
di: Zhang, Jinrui, et al.
Pubblicazione: (2024)

Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
di: Zhu, Xingyu, et al.
Pubblicazione: (2026)

Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
di: Yan, Bei, et al.
Pubblicazione: (2024)

Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding
di: Wang, Chao, et al.
Pubblicazione: (2025)

Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities
di: Datta, Shounak, et al.
Pubblicazione: (2025)

FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs
di: Yan, Bowen, et al.
Pubblicazione: (2024)

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
di: Zohrabi, Reihaneh, et al.
Pubblicazione: (2026)

Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating
di: Cheng, Zhe, et al.
Pubblicazione: (2026)

Review of Hallucination Understanding in Large Language and Vision Models
di: Ho, Zhengyi, et al.
Pubblicazione: (2025)

Detecting and Preventing Hallucinations in Large Vision Language Models
di: Gunjal, Anisha, et al.
Pubblicazione: (2023)

What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
di: Xie, Ming-Kun, et al.
Pubblicazione: (2025)

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
di: Wang, Weihang, et al.
Pubblicazione: (2025)

A Unified Hallucination Mitigation Framework for Large Vision-Language Models
di: Chang, Yue, et al.
Pubblicazione: (2024)

VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation
di: Zhang, Ruiyang, et al.
Pubblicazione: (2024)

IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
di: Zhu, Lanyun, et al.
Pubblicazione: (2024)

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
di: Neo, Dexter, et al.
Pubblicazione: (2024)

Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
di: Park, Dongmin, et al.
Pubblicazione: (2024)

A Survey on Hallucination in Large Vision-Language Models
di: Liu, Hanchao, et al.
Pubblicazione: (2024)

Mitigating Multilingual Hallucination in Large Vision-Language Models
di: Qu, Xiaoye, et al.
Pubblicazione: (2024)

HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models
di: Liu, Xinyun
Pubblicazione: (2026)

Benchmarking Deflection and Hallucination in Large Vision-Language Models
di: Moratelli, Nicholas, et al.
Pubblicazione: (2026)

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context
di: Gu, Zishan, et al.
Pubblicazione: (2024)

Revealing Multi-View Hallucination in Large Vision-Language Models
di: Park, Wooje, et al.
Pubblicazione: (2026)

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models
di: Wu, Junjie, et al.
Pubblicazione: (2024)

VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
di: Wang, Yuxuan, et al.
Pubblicazione: (2024)

Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?
di: Geigle, Gregor, et al.
Pubblicazione: (2024)

From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
di: Shang, Yuying, et al.
Pubblicazione: (2024)

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
di: Agrawal, Aakriti, et al.
Pubblicazione: (2025)

A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
di: Jing, Liqiang, et al.
Pubblicazione: (2025)