Copertina

Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Datta, Shounak, Sundararaman, Dhanasekar
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models
di: Kaul, Prannay, et al.
Pubblicazione: (2024)

Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
di: Xu, Yuanzhi, et al.
Pubblicazione: (2026)

Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models
di: Lee, Jihoon, et al.
Pubblicazione: (2025)

BiPrompt: Bilateral Prompt Optimization for Visual and Textual Debiasing in Vision-Language Models
di: Gupta, Sunny, et al.
Pubblicazione: (2026)

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
di: Wu, Junfei, et al.
Pubblicazione: (2024)

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

Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
di: Zhao, Linxi, et al.
Pubblicazione: (2024)

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
di: Tang, Kai, et al.
Pubblicazione: (2025)

Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
di: Fieback, Laura, et al.
Pubblicazione: (2025)

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering
di: Li, Zhuowei, et al.
Pubblicazione: (2025)

HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models
di: Yu, Le, et al.
Pubblicazione: (2025)

SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
di: Carragher, Peter, et al.
Pubblicazione: (2025)

Visual Hallucinations of Multi-modal Large Language Models
di: Huang, Wen, et al.
Pubblicazione: (2024)

Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection
di: Seo, Soo Won, et al.
Pubblicazione: (2026)

ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models
di: Park, Yeji, et al.
Pubblicazione: (2024)

Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
di: Han, Zongbo, et al.
Pubblicazione: (2024)

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
di: Tan, Shiwei, et al.
Pubblicazione: (2026)

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

Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
di: Yang, Qi, et al.
Pubblicazione: (2025)

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
di: Baniecki, Hubert, et al.
Pubblicazione: (2025)

Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models
di: Liu, Zhongye, et al.
Pubblicazione: (2024)

Reducing Hallucinations in Vision-Language Models via Latent Space Steering
di: Liu, Sheng, et al.
Pubblicazione: (2024)

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis
di: Huang, Po-Hsuan, et al.
Pubblicazione: (2024)

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

Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback
di: Zhou, Yucheng, et al.
Pubblicazione: (2025)

Adapting Vision-Language Models for Evaluating World Models
di: Hendriksen, Mariya, et al.
Pubblicazione: (2025)

Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads
di: Cherian, Anoop, et al.
Pubblicazione: (2024)

Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
di: Deng, Ailin, et al.
Pubblicazione: (2024)

Toward More Reliable Artificial Intelligence: Reducing Hallucinations in Vision-Language Models
di: Sanogo, Kassoum, et al.
Pubblicazione: (2025)

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

IDAL: Improved Domain Adaptive Learning for Natural Images Dataset
di: Gupta, Ravi Kant, et al.
Pubblicazione: (2025)

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

TopoPerception: A Shortcut-Free Evaluation of Global Visual Perception in Large Vision-Language Models
di: Zhou, Wenhao, et al.
Pubblicazione: (2025)

NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders
di: Dineva, Katarina Trojachanec, et al.
Pubblicazione: (2026)

Do Vision and Language Encoders Represent the World Similarly?
di: Maniparambil, Mayug, et al.
Pubblicazione: (2024)

Effectiveness Assessment of Recent Large Vision-Language Models
di: Jiang, Yao, et al.
Pubblicazione: (2024)

Parallel In-context Learning for Large Vision Language Models
di: Yamaguchi, Shin'ya, et al.
Pubblicazione: (2026)

How Culturally Aware are Vision-Language Models?
di: Burda-Lassen, Olena, et al.
Pubblicazione: (2024)

Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
di: Verma, Prateek, et al.
Pubblicazione: (2024)

Rethinking Post-Unlearning Behavior of Large Vision-Language Models
di: Kim, Minsung, et al.
Pubblicazione: (2025)