:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xia, Yuxuan, Wang, Siheng, Li, Peng
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	https://arxiv.org/abs/2601.03500
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
by: Wang, Xintong, et al.
Published: (2024)

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models
by: Lee, Yi-Lun, et al.
Published: (2024)

Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)
by: Manevich, Avshalom, et al.
Published: (2024)

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

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

Mitigating Hallucinations in Video Large Language Models via Spatiotemporal-Semantic Contrastive Decoding
by: Gao, Yuansheng, et al.
Published: (2026)

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

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

Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
by: Min, Kyungmin, et al.
Published: (2024)

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models
by: Chen, Xinlong, et al.
Published: (2025)

Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance
by: Chen, Xinrong, et al.
Published: (2026)

Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
by: Huo, Fushuo, et al.
Published: (2024)

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
by: Zhang, Chengsheng, et al.
Published: (2026)

Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
by: Zhang, Yuanhong, et al.
Published: (2026)

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

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
by: Dong, Xin, et al.
Published: (2025)

SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding
by: Wu, Chang-Hsun, et al.
Published: (2025)

CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
by: Kan, Zhehan, et al.
Published: (2024)

Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
by: Suo, Wei, et al.
Published: (2025)

MAP: Mitigating Hallucinations in Large Vision-Language Models with Map-Level Attention Processing
by: Li, Chenxi, et al.
Published: (2025)

Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models
by: Yin, Jianghao, et al.
Published: (2026)

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

V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization
by: Xie, Yuxi, et al.
Published: (2024)

Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models
by: Zou, Zhengtao, et al.
Published: (2025)

MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models
by: Chang, Aofei, et al.
Published: (2025)

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?
by: Yin, Hao, et al.
Published: (2025)

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

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

VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering
by: Wang, Zihu, et al.
Published: (2025)

Temporal Insight Enhancement: Mitigating Temporal Hallucination in Multimodal Large Language Models
by: Sun, Li, et al.
Published: (2024)

CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs
by: Huang, Xiaoyi, et al.
Published: (2026)

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

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

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

Learning to Decode Against Compositional Hallucination in Video Multimodal Large Language Models
by: Xing, Wenbin, et al.
Published: (2026)

NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors
by: Ren, Lingfeng, et al.
Published: (2026)

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models
by: Tran, Huu-Thien, et al.
Published: (2025)

Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
by: Hu, Rui, et al.
Published: (2024)

CoFi-Dec: Hallucination-Resistant Decoding via Coarse-to-Fine Generative Feedback in Large Vision-Language Models
by: Cao, Zongsheng, et al.
Published: (2025)