Cover Image

Mitigating Multimodal Hallucinations via Gradient-based Self-Reflection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Shan, Shen, Maying, Chang, Nadine, Nguyen, Chuong, Li, Hongdong, Alvarez, Jose M.
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation
by: Shen, Maying, et al.
Published: (2024)

Mitigating Multimodal Hallucination via Phase-wise Self-reward
by: Zhang, Yu, et al.
Published: (2026)

Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
by: Lee, Seongyun, et al.
Published: (2023)

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations
by: Lu, Yifan, 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)

Mitigating Object Hallucination via Concentric Causal Attention
by: Xing, Yun, et al.
Published: (2024)

Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
by: Yue, Zihao, et al.
Published: (2024)

Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
by: Fu, Yuhan, et al.
Published: (2024)

GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction
by: Tong, Jinguang, et al.
Published: (2025)

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
by: Deng, Haolin, et al.
Published: (2026)

ReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
by: Yang, Jianjiang, et al.
Published: (2025)

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
by: Xing, Shangyu, et al.
Published: (2024)

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

Mitigating Object Hallucination via Robust Local Perception Search
by: Gao, Zixian, et al.
Published: (2025)

ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
by: Kim, Minchan, et al.
Published: (2024)

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

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

Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization
by: Compagnoni, Alberto, et al.
Published: (2025)

Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
by: Zhang, Ce, et al.
Published: (2025)

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
by: You, Liangliang, 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)

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

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
by: Li, Shuo, et al.
Published: (2025)

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

CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base
by: Nguyen, Cong-Duy, et al.
Published: (2025)

Do Vision Encoders Truly Explain Object Hallucination?: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore
by: Oh, Hongseok, et al.
Published: (2025)

Generalized Trajectory Scoring for End-to-end Multimodal Planning
by: Li, Zhenxin, et al.
Published: (2025)

Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens
by: Zheng, Haohan, et al.
Published: (2025)

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

Analyzing and Mitigating Object Hallucination: A Training Bias Perspective
by: Li, Yifan, et al.
Published: (2025)

Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting
by: Wu, Jiarui, et al.
Published: (2025)

Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models
by: Li, Bin, et al.
Published: (2025)

Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
by: Nguyen, Hoang Chuong, et al.
Published: (2024)

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
by: Dimlioglu, Tolga, et al.
Published: (2026)

Systematic Reward Gap Optimization for Mitigating VLM Hallucinations
by: He, Lehan, et al.
Published: (2024)

Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
by: Chen, Boqi, et al.
Published: (2026)

HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding
by: Yuan, Fan, et al.
Published: (2024)

Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
by: Fazli, Mehrdad, et al.
Published: (2025)

Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination
by: Li, Xinzhuo, et al.
Published: (2025)

MARS: Memory-Enhanced Agents with Reflective Self-improvement
by: Liang, Xuechen, et al.
Published: (2025)