Cover Image

Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shinozaki, Taiga, Doi, Tomoki, Watahiki, Amane, Nishida, Satoshi, Yanaka, Hitomi
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

Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports
by: Watahiki, Amane, et al.
Published: (2025)

Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese
by: Watahiki, Amane, et al.
Published: (2026)

What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?
by: Ryu, Koki, et al.
Published: (2026)

Comprehensive Evaluation of Large Language Models for Topic Modeling
by: Doi, Tomoki, et al.
Published: (2024)

Investigating Training and Generalization in Faithful Self-Explanations of Large Language Models
by: Doi, Tomoki, et al.
Published: (2025)

The Illusion-Illusion: Vision Language Models See Illusions Where There are None
by: Ullman, Tomer
Published: (2024)

IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
by: Shahgir, Haz Sameen, et al.
Published: (2024)

Joint Modeling of Big Five and HEXACO for Multimodal Apparent Personality-trait Recognition
by: Masumura, Ryo, et al.
Published: (2025)

Derivational Probing: Unveiling the Layer-wise Derivation of Syntactic Structures in Neural Language Models
by: Someya, Taiga, et al.
Published: (2025)

HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
by: Guan, Tianrui, et al.
Published: (2023)

Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models
by: Kumon, Ryoma, et al.
Published: (2026)

Can Large Language Models Robustly Perform Natural Language Inference for Japanese Comparatives?
by: Mikami, Yosuke, et al.
Published: (2025)

Vision-Language Models Do Not Understand Negation
by: Alhamoud, Kumail, et al.
Published: (2025)

Do Vision-Language Models Really Understand Visual Language?
by: Hou, Yifan, et al.
Published: (2024)

Do Vision-Language Models Understand Compound Nouns?
by: Kumar, Sonal, et al.
Published: (2024)

Do Vision-Language Models Understand Visual Persuasiveness?
by: Park, Gyuwon
Published: (2025)

Neuron-Level Analysis of Cultural Understanding in Large Language Models
by: Yamamoto, Taisei, et al.
Published: (2025)

Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment
by: Wang, Kangsheng, et al.
Published: (2025)

CLIP-Adapter: Better Vision-Language Models with Feature Adapters
by: Gao, Peng, et al.
Published: (2021)

Do Multimodal Large Language Models Understand Welding?
by: Khvatskii, Grigorii, et al.
Published: (2025)

Intriguing Properties of Large Language and Vision Models
by: Lee, Young-Jun, et al.
Published: (2024)

Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features
by: Mitra, Chancharik, et al.
Published: (2024)

Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
by: Xu, Yige, et al.
Published: (2026)

Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
by: Liu, Zikang, et al.
Published: (2025)

Analyzing the Inner Workings of Transformers in Compositional Generalization
by: Kumon, Ryoma, et al.
Published: (2025)

Enhancing Rating Prediction with Off-the-Shelf LLMs Using In-Context User Reviews
by: Ryu, Koki, et al.
Published: (2025)

NeuronMoE: Neuron-Guided Mixture-of-Experts for Efficient Multilingual LLM Extension
by: Li, Rongzhi, et al.
Published: (2026)

Instruction-Following Evaluation of Large Vision-Language Models
by: Shiono, Daiki, et al.
Published: (2025)

Vision-centric Token Compression in Large Language Model
by: Xing, Ling, et al.
Published: (2025)

Visual In-Context Learning for Large Vision-Language Models
by: Zhou, Yucheng, et al.
Published: (2024)

Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
by: Rostamkhani, Mohammadmostafa, et al.
Published: (2024)

Beyond the Vision Encoder: Identifying and Mitigating Spatial Bias in Large Vision-Language Models
by: Zhu, Yingjie, et al.
Published: (2025)

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

PUMGPT: A Large Vision-Language Model for Product Understanding
by: Xue, Wei, et al.
Published: (2023)

Toward Interactive Regional Understanding in Vision-Large Language Models
by: Lee, Jungbeom, et al.
Published: (2024)

Can Large Vision-Language Models Understand Multimodal Sarcasm?
by: Wang, Xinyu, et al.
Published: (2025)

Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
by: Lu, Jiaying, et al.
Published: (2023)

Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
by: Geigle, Gregor, et al.
Published: (2025)

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models
by: Zhang, Yiming, et al.
Published: (2025)

NPHardEval4V: Dynamic Evaluation of Large Vision-Language Models with Effects of Vision
by: Li, Xiang, et al.
Published: (2024)