Copertina

Mind the Uncertainty in Human Disagreement: Evaluating Discrepancies between Model Predictions and Human Responses in VQA

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lan, Jian, Frassinelli, Diego, Plank, Barbara
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language
di: Kim, Yoonshik, et al.
Pubblicazione: (2025)

Improving Automatic VQA Evaluation Using Large Language Models
di: Mañas, Oscar, et al.
Pubblicazione: (2023)

WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences
di: Lu, Yujie, et al.
Pubblicazione: (2024)

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA
di: Fan, Yue, et al.
Pubblicazione: (2024)

Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA
di: Karim, A H M Rezaul, et al.
Pubblicazione: (2025)

GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation
di: Zong, Yi, et al.
Pubblicazione: (2024)

DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning
di: Sivakumaran, Nithin, et al.
Pubblicazione: (2025)

MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
di: Baek, Jeonghun, et al.
Pubblicazione: (2025)

Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models
di: Zhu, Wenhui, et al.
Pubblicazione: (2025)

Knowledge Generation for Zero-shot Knowledge-based VQA
di: Cao, Rui, et al.
Pubblicazione: (2024)

Visual Grounding Methods for VQA are Working for the Wrong Reasons!
di: Shrestha, Robik, et al.
Pubblicazione: (2020)

Towards Predicting Any Human Trajectory In Context
di: Fujii, Ryo, et al.
Pubblicazione: (2025)

V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction
di: Zhao, Yiming, et al.
Pubblicazione: (2025)

GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection
di: Zhang, Shuguang, et al.
Pubblicazione: (2026)

Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling
di: Zou, Hongjian, et al.
Pubblicazione: (2026)

Adversarial Attacks on VQA-NLE: Exposing and Alleviating Inconsistencies in Visual Question Answering Explanations
di: Yeh, Yahsin, et al.
Pubblicazione: (2025)

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
di: Ghosh, Shiv, et al.
Pubblicazione: (2026)

Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants
di: Qin, Lixiong, et al.
Pubblicazione: (2025)

MindCube: Spatial Mental Modeling from Limited Views
di: Wang, Qineng, et al.
Pubblicazione: (2025)

Describing Images $\textit{Fast and Slow}$: Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes
di: Takmaz, Ece, et al.
Pubblicazione: (2024)

MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis
di: Chen, Lei, et al.
Pubblicazione: (2024)

HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
di: Kang, Zhaolu, et al.
Pubblicazione: (2025)

RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
di: Butsanets, Léo, et al.
Pubblicazione: (2025)

VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
di: Park, Seongheon, et al.
Pubblicazione: (2026)

EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing
di: Wu, Keming, et al.
Pubblicazione: (2025)

MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection
di: Gu, Hexiang, et al.
Pubblicazione: (2025)

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations
di: Srivastava, Archita, et al.
Pubblicazione: (2025)

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding
di: Faure, Gueter Josmy, et al.
Pubblicazione: (2026)

Unveiling the "Fairness Seesaw": Discovering and Mitigating Gender and Race Bias in Vision-Language Models
di: Lan, Jian, et al.
Pubblicazione: (2025)

Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models
di: Kim, Hyungjin, et al.
Pubblicazione: (2025)

Pixels, Patterns, but No Poetry: To See The World like Humans
di: Gao, Hongcheng, et al.
Pubblicazione: (2025)

Responses Fall Short of Understanding: Revealing the Gap between Internal Representations and Responses in Visual Document Understanding
di: Kawasaki, Haruka, et al.
Pubblicazione: (2026)

Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models
di: Lopez-Cardona, Angela, et al.
Pubblicazione: (2024)

Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge
di: Wang, Haibo, et al.
Pubblicazione: (2024)

DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images
di: Yim, Wen-wai, et al.
Pubblicazione: (2025)

NegVQA: Can Vision Language Models Understand Negation?
di: Zhang, Yuhui, et al.
Pubblicazione: (2025)

How Confident are Video Models? Empowering Video Models to Express their Uncertainty
di: Mei, Zhiting, et al.
Pubblicazione: (2025)

T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models
di: Guo, Xuyang, et al.
Pubblicazione: (2025)

Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
di: Mo, Wentao, et al.
Pubblicazione: (2024)

Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
di: Kim, Yunsoo, et al.
Pubblicazione: (2024)