Vision-Language Models (VLMs) transfer visual and textual data into a shared embedding space. In so doing, they enable a wide range of multimodal tasks, while also raising critical questions about the nature of machine 'understanding.' In this paper, we examine how Explainable Artificial Intelligence (XAI) methods can render the visual reasoning of a VLM - namely, CLIP - legible in art-historical contexts. To this end, we evaluate seven methods, combining zero-shot localization experiments with human interpretability studies. Our results indicate that, while these methods capture some aspects of human interpretation, their effectiveness hinges on the conceptual stability and representational availability of the examined categories.
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability
Northwestern University（西北大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Bristol（布里斯托大学）；Cisco Research（思科研究）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models
Tuebingen AI Center University of Tuebingen（图宾根人工智能中心图宾根大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation
Centre for Perceptual and Interactive Intelligence, the Chinese University of Hong Kong（香港中文大学感知与交互智能中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Department of Electronic Engineering, the Chinese University of Hong Kong（香港中文大学电子工程系）
MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence
AIDAS Laboratory, Seoul National University（首尔大学AIDAS实验室）；Samsung Changwon Hospital（三星昌原医院）；Samsung Medical Center（三星医疗中心）；NVIDIA, Santa Clara, USA（英伟达（美国圣克拉拉））
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
Department of Informatics, University of Salerno（萨勒诺大学信息学院）；Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology（挪威科技大学信息安全部）
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives
School of Computer Science（计算机科学学院）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）；Okinawa Institute of Science and Technology (OIST)（冲绳科学技术大学院大学）
Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
UiT The Arctic University of Norway（乌塔大学极地大学）；Technical University of Denmark（技术大学）；Østfold Hospital Trust（奥斯fold医院信托）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Radboud University Nijmegen Medical Centre（拉德堡德大学奈梅亨医疗中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Antoni van Leeuwenhoek Hospital（安东尼·弗莱明医院）；University of Copenhagen（哥本哈根大学）
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
University of Bonn（波恩大学）；Goethe University Frankfurt（法兰克福大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
本文提出面向 ViT 的可解释性方法 LeGrad，利用各层注意力图梯度并聚合多层 token 激活生成解释图，在分割、扰动和开放词表任务中展现出更优的空间保真度与鲁棒性。
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Hunan University（湖南大学）
Representing visual classification as a linear combination of words
Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Reedy High School（里迪高中）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（DarwinAI公司）
In this study, we take a departure and explore an explainability-driven strategy to data auditing, where actionable insights into the data at hand are discovered through the eyes of quantitative explainability on the behaviour of a dummy model prototype when exposed to data. We demonstrate this strategy by auditing two popular medical benchmark datasets, and discover hidden data quality issues that lead deep learning models to make predictions for the wrong reasons. The actionable insights gained from this explainability driven data auditing strategy is then leveraged to address the discovered…
This survey reviews explainability methods for vision-based self-driving systems trained with behavior cloning. The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application. Gathering contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI), this survey tackles several points. First, it discusses definitions, context, and motivation for gaining more interpretability and explainability from self-driving systems, as well as the challenges that are specific t…
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
Massachusetts Institute of Technology（麻省理工学院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
University of New Brunswick（新不伦瑞克大学）；Quy Nhon AI, FPT Software（FPT软件公司归仁人工智能部门）；Friedrich-Alexander-University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Software AG（软件AG公司）
LangXAI 框架将 XAI 与先进视觉模型结合，为分类、检测和分割任务生成文本解释，以高 BERTScore 验证其合理性，从而提升终端用户对视觉 AI 的理解与信任。
Deep learning based computer vision models are increasingly used by urban planners to support decision making for shaping urban environments. Such models predict how people perceive the urban environment quality in terms of e.g. its safety or beauty. However, the blackbox nature of deep learning models hampers urban planners to understand what landscape objects contribute to a particularly high quality or low quality urban space perception. This study investigates how computer vision models can be used to extract relevant policy information about peoples' perception of the urban space. To do s…
Does Explainability Transfer? A Controlled Benchmark of Attribution Methods on Vision Transformers and CNNs
Faculty of Computing, Sabaragamuwa University of Sri Lanka（斯里兰卡萨巴拉加穆瓦大学计算机学院）；Faculty of Engineering, University of Moratuwa（莫拉图瓦大学工程学院）；School of Computing Technologies, RMIT University（RMIT大学计算技术学院）；School of Engineering & Digital Technologies, University of Southern Queensland（南昆士兰大学工程与数字技术学院）；School of Engineering & Technologies, UNSW（新南威尔士大学工程与技术学院）；Faculty of Science and Technology, Charles Darwin University（查尔斯达尔文大学科学技术学院）
Symbiosis Institute of Technology (SIT)（共生工程技术学院（SIT））；Symbiosis International (Deemed University)（共生国际大学）；Symbiosis Centre for Applied Artificial Intelligence（共生应用人工智能中心）
Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and …
In the computer vision community, Convolutional Neural Networks (CNNs), first proposed in the 1980's, have become the standard visual classification model. Recently, as alternatives to CNNs, Capsule Networks (CapsNets) and Vision Transformers (ViTs) have been proposed. CapsNets, which were inspired by the information processing of the human brain, are considered to have more inductive bias than CNNs, whereas ViTs are considered to have less inductive bias than CNNs. All three classification models have received great attention since they can serve as backbones for various downstream tasks. How…
Printed Circuit Board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods, however they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In p…
