Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Shiyu, Wen, Xinyi, Lan, Zhibin, Wang, Ante, Su, Jinsong |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
by: Sun, Han, et al.
Published: (2026)
by: Sun, Han, et al.
Published: (2026)
Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs
by: Yu, Liu, et al.
Published: (2025)
by: Yu, Liu, et al.
Published: (2025)
Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation
by: Li, Wenhao, et al.
Published: (2025)
by: Li, Wenhao, et al.
Published: (2025)
MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs
by: Ding, Wei, et al.
Published: (2026)
by: Ding, Wei, et al.
Published: (2026)
Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction
by: Lyu, Guangtao, et al.
Published: (2025)
by: Lyu, Guangtao, et al.
Published: (2025)
Boosting Visual Knowledge-Intensive Training for LVLMs Through Causality-Driven Visual Object Completion
by: Hu, Qingguo, et al.
Published: (2025)
by: Hu, Qingguo, et al.
Published: (2025)
MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs
by: Ge, Haonan, et al.
Published: (2025)
by: Ge, Haonan, et al.
Published: (2025)
HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
by: Akl, Ahmed, et al.
Published: (2026)
by: Akl, Ahmed, et al.
Published: (2026)
Optimizing LVLMs with On-Policy Data for Effective Hallucination Mitigation
by: Yu, Chengzhi, et al.
Published: (2025)
by: Yu, Chengzhi, et al.
Published: (2025)
TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection
by: Jiang, Lei, et al.
Published: (2025)
by: Jiang, Lei, et al.
Published: (2025)
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
by: Hua, Zhenglin, et al.
Published: (2025)
by: Hua, Zhenglin, et al.
Published: (2025)
Towards Interpretable Hallucination Analysis and Mitigation in LVLMs via Contrastive Neuron Steering
by: Lyu, Guangtao, et al.
Published: (2026)
by: Lyu, Guangtao, et al.
Published: (2026)
GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity
by: Park, Seongheon, et al.
Published: (2025)
by: Park, Seongheon, et al.
Published: (2025)
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
by: Kan, Zhehan, et al.
Published: (2024)
by: Kan, Zhehan, et al.
Published: (2024)
Attention-space Contrastive Guidance for Efficient Hallucination Mitigation in LVLMs
by: Jo, Yujin, et al.
Published: (2026)
by: Jo, Yujin, et al.
Published: (2026)
Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens
by: Zheng, Haohan, et al.
Published: (2025)
by: Zheng, Haohan, et al.
Published: (2025)
Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement
by: Qin, Zhenxin, et al.
Published: (2026)
by: Qin, Zhenxin, et al.
Published: (2026)
Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)
by: Manevich, Avshalom, et al.
Published: (2024)
by: Manevich, Avshalom, et al.
Published: (2024)
Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
by: Xie, Yutong, et al.
Published: (2026)
by: Xie, Yutong, et al.
Published: (2026)
Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models
by: Hu, Nanxing, et al.
Published: (2025)
by: Hu, Nanxing, et al.
Published: (2025)
SAVAA: Mitigating Hallucinations in LVLMs via Step-wise Adaptive Visual Attention Amplification
by: Zhang, Jiacheng, et al.
Published: (2026)
by: Zhang, Jiacheng, et al.
Published: (2026)
What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness
by: He, Yusheng, et al.
Published: (2026)
by: He, Yusheng, et al.
Published: (2026)
PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models
by: Zhuang, Wanru, et al.
Published: (2025)
by: Zhuang, Wanru, et al.
Published: (2025)
PANICL: Mitigating Over-Reliance on Single Prompt in Visual In-Context Learning
by: Zhang, Jiahao, et al.
Published: (2025)
by: Zhang, Jiahao, et al.
Published: (2025)
CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs
by: Huang, Xiaoyi, et al.
Published: (2026)
by: Huang, Xiaoyi, et al.
Published: (2026)
Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
by: Zhu, Younan, et al.
Published: (2025)
by: Zhu, Younan, et al.
Published: (2025)
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
by: Liu, Yufang, et al.
Published: (2024)
by: Liu, Yufang, et al.
Published: (2024)
MDSAM:Memory-Driven Sparse Attention Matrix for LVLMs Hallucination Mitigation
by: Lu, Shuaiye, et al.
Published: (2025)
by: Lu, Shuaiye, et al.
Published: (2025)
See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs
by: Dai, Ziyun, et al.
Published: (2025)
by: Dai, Ziyun, et al.
Published: (2025)
Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation
by: Chen, Beitao, et al.
Published: (2025)
by: Chen, Beitao, et al.
Published: (2025)
Hallucinatory Image Tokens: A Training-free EAZY Approach on Detecting and Mitigating Object Hallucinations in LVLMs
by: Che, Liwei, et al.
Published: (2025)
by: Che, Liwei, et al.
Published: (2025)
Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs
by: Zhang, Xiaofeng, et al.
Published: (2024)
by: Zhang, Xiaofeng, et al.
Published: (2024)
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
by: Li, Shuo, et al.
Published: (2025)
by: Li, Shuo, et al.
Published: (2025)
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
by: Ghosh, Sreyan, et al.
Published: (2024)
by: Ghosh, Sreyan, et al.
Published: (2024)
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
by: Li, Wenbo, et al.
Published: (2024)
by: Li, Wenbo, et al.
Published: (2024)
Kestrel: Grounding Self-Refinement for LVLM Hallucination Mitigation
by: Mao, Jiawei, et al.
Published: (2026)
by: Mao, Jiawei, et al.
Published: (2026)
AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity
by: Lan, Zhibin, et al.
Published: (2024)
by: Lan, Zhibin, et al.
Published: (2024)
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
by: Lan, Zhibin, et al.
Published: (2025)
by: Lan, Zhibin, et al.
Published: (2025)
Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment
by: Chang, Kai-Po, et al.
Published: (2025)
by: Chang, Kai-Po, et al.
Published: (2025)
Mitigating Cross-Image Information Leakage in LVLMs for Multi-Image Tasks
by: Park, Yeji, et al.
Published: (2025)
by: Park, Yeji, et al.
Published: (2025)
Similar Items
-
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
by: Sun, Han, et al.
Published: (2026) -
Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs
by: Yu, Liu, et al.
Published: (2025) -
Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation
by: Li, Wenhao, et al.
Published: (2025) -
MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs
by: Ding, Wei, et al.
Published: (2026) -
Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction
by: Lyu, Guangtao, et al.
Published: (2025)