Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. The code and data are available at https://github.com/vl-illusion/dataset.
Beyond Shortcuts: Mitigating Visual Illusions in Frozen VLMs via Qualitative Reasoning
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥国家科学中心）；Anhui Polytechnic University（安徽理工大学）；Hefei University of Technology（合肥工业大学）；Anhui University（安徽大学）；IGS, Imperial College London（帝国理工学院伦敦分校）
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems
Computational Linguistics, Department of Linguistics, Bielefeld University（语言学计算系，语言学系，比勒菲尔德大学）；Honda Research Institute Europe（本田欧洲研究院）；Digital Linguistics Lab, Department of Linguistics, Bielefeld University（数字语言学实验室，语言学系，比勒菲尔德大学）
Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination
University of Southern California（南加州大学）；University of California San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Do Large Language Models Show Biases in Causal Learning?
UBA（布宜诺斯艾利斯大学）；Università degli Studi di Genova（热那亚大学）；University of Buenos Aires（布宜诺斯艾利斯大学）；Universidad Nacional del Sur（南方国立大学）；IIIA-CSIC（西班牙国家研究委员会人工智能研究所）；Universidad Autónoma de Barcelona（巴塞罗那自治大学）；Arizona State University（亚利桑那州立大学）
Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding
East China University of Science and Technology（华东理工大学）；Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）
ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision
IBM Research Zurich, Zurich, Switzerland（IBM苏黎世研究实验室，瑞士苏黎世）；ETH Zurich, Computer Vision（苏黎世联邦理工学院，计算机视觉）；ETH AI Center, Switzerland（苏黎世联邦理工学院人工智能中心，瑞士）；ETH Zurich, Photogrammetry（苏黎世联邦理工学院，摄影测量学）；Microsoft, Switzerland（微软公司，瑞士）
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
Shanghai AI Lab（上海人工智能实验室）；Dalian University of Technology（大连理工大学）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
提出 3DAxisPrompt，通过 3D 坐标轴与 SAM 掩码为 GPT-4o 提供几何先验，并在四个数据集上验证其 3D 定位与推理能力。
Motion Illusions Generated Using Predictive Neural Networks Also Fool Humans
Sony Computer Science Laboratories, Kyoto Laboratory, Japan（索尼计算机科学实验室，京都实验室，日本）；Laboratory of Neurophysiology, National Institute for Basic Biology, Okazaki, Japan（神经生理学实验室，基础生物学国家研究所，冈崎，日本）
