Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation
City University of Hong Kong（香港城市大学）；Shenzhen University（深圳大学）；University of Rochester（罗切斯特大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；The Chinese University of Hong Kong（香港中文大学）
To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with corresponding words. To capture the abnormal regions of varying sizes and positions, we introduce the Adaptive Patch extraction (AdaPatch) module to acquire the adaptive patches for these regions adaptively. In order to provide explicit explainability for CXR-report generation task, we propose an AdaMatch-based bidirectional large language model for Cyclic CXR-report generation (AdaMatch-Cyclic). It employs the AdaMatch to obtain the keywords for CXR images and `keypatches' for medical reports as hints to guide CXR-report generation. Extensive experiments on two publicly available CXR datasets prove the effectiveness of our method and its superior performance to existing methods.
Indian Institute of Technology (IIT) Ropar（印度理工学院罗巴尔分校）；Monash University（莫纳什大学）；University of Canberra（堪培拉大学）；Inception Institute of Artificial Intelligence（因塞普申人工智能研究院）
We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we novelly generate congruent image--report pairs employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the generated report will adequately explain a medical image, while a report-generated image that effectively characterizes the text visually should (sufficientl…
FINER: MLLMs Hallucinate under Fine-grained Negative Queries
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Munich（海德堡-慕尼黑亥姆霍尔茨中心）；Google（谷歌）；LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎电信学院，巴黎理工学院）
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Hong Kong Baptist University（香港浸会大学）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）；University of Science and Technology Beijing（北京科技大学）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence（知识驱动人机智能教育部工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际合作中心）
University of Mannheim（曼海姆大学）；University of Technology Nuremberg（纽伦堡技术大学）；National Institute of Information and Communications Technology（信息与通信技术国家研究所）
Tsinghua University（清华大学）；BNRist；OPPO AI Center（OPPO人工智能中心）；Peking University（北京大学）；Hangzhou Zhuoxi Institute of Brain and Intelligence（杭州智行脑科学与人工智能研究所）
Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model
Harbin Institute of Technology（哈尔滨工程大学）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；University of Science and Technology of China（中国科学技术大学）
EDVD-LLaMA: Explainable Deepfake Video Detection via Multimodal Large Language Model Reasoning
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电子与电气工程系，香港理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）
Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Tongyi Lab, Alibaba Group（阿里云实验室）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
School of Computer Science and Engineering, UESTC, Chengdu, China（电子科技大学计算机科学与工程学院，成都，中国）；IDEAS Lab, University of Birmingham, Birmingham, UK（伯明翰大学IDEAS实验室，英国布里斯托尔）
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. Because we lack fine-grained labels to train our method, we propose a novel multi-instance learning approach which learns a fine-grained labeling u…
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Nanyang Technological University（南洋理工大学）；Ant Digital Technologies, Ant Group（蚂蚁数字科技，蚂蚁集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Management University（新加坡管理大学）
