:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jia, Kaidi, Lin, Yujie, Yang, Chengyi, Ma, Jiayao, Su, Jinsong
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2605.08031
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multi-Object Hallucination in Vision-Language Models
by: Chen, Xuweiyi, et al.
Published: (2024)

Dual-Pathway Circuits of Object Hallucination in Vision-Language Models
by: Liu, Jiaxin, et al.
Published: (2026)

Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
by: Yang, Le, et al.
Published: (2024)

Do Not Leave a Gap: Hallucination-Free Object Concealment in Vision-Language Models
by: Guesmi, Amira, et al.
Published: (2026)

TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Intervention
by: Duan, Jinhao, et al.
Published: (2025)

Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation
by: Li, Qiming, et al.
Published: (2025)

Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding
by: Ma, Ruiqi, et al.
Published: (2025)

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

Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
by: Lovenia, Holy, et al.
Published: (2023)

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

Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework
by: Liu, Shiyu, et al.
Published: (2026)

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

DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
by: Wang, JiYang, et al.
Published: (2026)

Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
by: Zhou, Yiyang, et al.
Published: (2023)

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

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

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

Visual-Guided Key-Token Regularization for Multimodal Large Language Model Unlearning
by: Cai, Chengyi, et al.
Published: (2026)

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

Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
by: Liu, Yufang, et al.
Published: (2024)

CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
by: Anand, Neeraj, et al.
Published: (2026)

Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
by: An, Wenbin, et al.
Published: (2024)

CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models
by: Li, Qiming, et al.
Published: (2025)

Multi-Object Grounding via Hierarchical Contrastive Siamese Transformers
by: Du, Chengyi, et al.
Published: (2025)

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
by: Ma, Yingzi, et al.
Published: (2024)

Online Self-Calibration Against Hallucination in Vision-Language Models
by: Chen, Minghui, et al.
Published: (2026)

ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models
by: Chen, Junzhe, et al.
Published: (2024)

Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens
by: Jiang, Zhangqi, et al.
Published: (2024)

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

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
by: Zhu, Younan, et al.
Published: (2025)

Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
by: Woo, Sangmin, et al.
Published: (2025)

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

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

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

Training-Free Semantic Multi-Object Tracking with Vision-Language Models
by: Bonat, Laurence, et al.
Published: (2026)

Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations
by: Chen, Boxu, et al.
Published: (2025)

On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models
by: Seo, Hoigi, et al.
Published: (2025)

Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models
by: Song, Jiale, et al.
Published: (2026)

Boosting Visual Knowledge-Intensive Training for LVLMs Through Causality-Driven Visual Object Completion
by: Hu, Qingguo, et al.
Published: (2025)

Cross-Modal Attention Guided Unlearning in Vision-Language Models
by: Bhaila, Karuna, et al.
Published: (2025)