A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model's respective predictions remains unclear. We conducted a user study (N=240) to test how such a baseline explanation technique performs against concept-based and counterfactual explanations. To this end, we contribute a synthetic dataset generator capable of biasing individual attributes and quantifying their relevance to the model. In a study, we assess if participants can identify the relevant set of attributes compared to the ground-truth. Our results show that the baseline outperformed concept-based explanations. Counterfactual explanations from an invertible neural network performed similarly as the baseline. Still, they allowed users to identify some attributes more accurately. Our results highlight the importance of measuring how well users can reason about biases of a model, rather than solely relying on technical evaluations or proxy tasks. We open-source our study and dataset so it can serve as a blue-print for future studies. For code see, https://github.com/berleon/do_users_benefit_from_interpretable_vision
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Cyber Security, Jinan University（暨南大学网络安全学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
UMIT TIROL – Private University for Health Sciences and Health Technology（UMIT TIROL – 健康科学与健康技术私立大学）；VASCage – Centre on Clinical Stroke Research（VASCage – 临床卒中研究中心）；Medical University of Innsbruck（因斯布鲁克医科大学）
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
University of Bath（巴斯大学）；Zhejiang University（浙江大学）
Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
School of Computing University of Connecticut Storrs, CT（计算学院 美国康涅狄格大学 斯托尔斯分校）；Department of Machine Learning Research Morgan Stanley New York, NY（机器学习研究部 花旗集团 新 York）
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Zhejiang University（浙江大学）；University of Illinois Urbana–Champaign Institute（伊利诺伊大学厄巴纳-香槟分校研究院）；Sixth Medical Center of Chinese PLA General Hospital（中国人民解放军总医院第六医学中心）
Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack
Department of Computer Science and Engineering, Sungkyunkwan University（计算机科学与工程系，全州大学）；Department of Computer Science, Loyola University（计算机科学系，洛伊拉大学）
When Does Perceptual Alignment Benefit Vision Representations?
MIT（麻省理工学院）；Google DeepMind（谷歌DeepMind）；TU Berlin（柏林工业大学）；BIFOLD（柏林大数据中心）；Weizmann Institute of Science（魏茨曼科学研究所）；Anthropic（Anthropic公司）；U.C. Berkeley（加州大学伯克利分校）
How Much Can CLIP Benefit Vision-and-Language Tasks?
University of California, Berkeley（加州大学伯克利分校）；University of California, Los Angeles（加州大学洛杉矶分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Sparse Autoencoders for Interpretable Medical Image Representation Learning
Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学人工智能医学与成像中心）；Department of Radiology, Stanford University（斯坦福大学放射科）；Chair of AI in Healthcare and Medicine, Technical University of Munich（慕尼黑技术大学人工智能医疗与医学主任）；TUM University Hospital, Munich, Germany（慕尼黑技术大学医院）
Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models
Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学学院）；Doctoral School of Exact and Natural Sciences, Jagiellonian University（雅盖隆大学精确与自然科学博士学校）；Centre for Credible AI, Warsaw University of Technology（华沙技术大学可信人工智能中心）
