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References (79)

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Qwen2.5-VL Technical Report
2025Shuai Bai, Keqin Chen et al.
[2]
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
2024Peng Wang, Shuai Bai et al.
[3]
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
2024Shiping Liu, Kecheng Zheng et al.
[4]
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
2024Xintong Wang, Jingheng Pan et al.
[5]
Multi-Modal Hallucination Control by Visual Information Grounding
2024Alessandro Favero, L. Zancato et al.
[6]
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
2024Ke Wang, Junting Pan et al.
[7]
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation
2023Qidong Huang, Xiao-wen Dong et al.
[8]
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
2023Sicong Leng, Hang Zhang et al.
[9]
An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
2023Junyang Wang, Yuhang Wang et al.
[10]
Woodpecker: hallucination correction for multimodal large language models
2023Shukang Yin, Chaoyou Fu et al.
[11]
Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
2023Tianrui Guan, Fuxiao Liu et al.
[12]
Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
2023Holy Lovenia, Wenliang Dai et al.
[13]
Module-wise Adaptive Distillation for Multimodality Foundation Models
2023Chen Liang, Jiahui Yu et al.
[14]
Improved Baselines with Visual Instruction Tuning
2023Haotian Liu, Chunyuan Li et al.
[15]
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
2023Long Chen, Oleg Sinavski et al.
[16]
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
2023Yiyang Zhou, Chenhang Cui et al.
[17]
Qwen Technical Report
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[18]
Aligning Large Multimodal Models with Factually Augmented RLHF
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[19]
Contrastive Decoding Improves Reasoning in Large Language Models
2023Sean O'Brien, Mike Lewis
[20]
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks
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Showing 20 of 79 references

Founder's Pitch

"Innovative framework NoLaN reduces object hallucinations in Vision-Language Models by dynamically suppressing language priors."

Vision-Language ModelsScore: 7View PDF ↗

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10

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Why It Matters

Summary from abstract: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Product Angle

Product angle: NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

Disruption

Disruption: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Product Opportunity

Opportunity: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Use Case Idea

Potential use case: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Science

Technical summary: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Method & Eval

Method and evaluation details: Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline prim

Caveats

Caveats not specified in the abstract.

Author Intelligence

Research Author 1

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Research Author 2

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Research Author 3

University / Research Lab
author@institution.edu