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检索条件"机构=Software Engineering with Multimedia"

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Pioneering Explainable Video Fact-Checking with a New Dataset and Multi-role Multimodal Model Approach 39

Pioneering Explainable Video Fact-Checking with a New Datase...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Niu, Kaipeng Xu, Danni Yang, Bingjian Liu, Wenxuan Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China National University of Singapore Singapore Singapore Peking University China

ISBN: (纸本)157735897X

Existing video fact-checking datasets often lack detailed evidence and explanations, compromising the reliability and interpretability of fact-checking methods. To address these gaps, we developed a novel dataset featuring comprehensive annotations for each news item, including veracity labels, the rationales behind these labels, and supporting evidence. This dataset significantly enhances models’ ability to accurately identify and explain video content. We also present an explainable automatic framework 3MFact, utilizing Multi-role Multimodal Models for video Fact-checking. Our framework iteratively gathers and synthesizes online evidence to progressively determine the veracity label, generating three key outputs: veracity label, rationale, and supported evidence. We aim for this work to be a pioneering effort, providing robust support for the field of video fact-checking. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Enhanced Bidirectional GRUs with Self-attention for Medical Image Report Generation: A Deep Learning Approach 8th

Enhanced Bidirectional GRUs with Self-attention for Medica...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Agarwal, Saurabh Yashwanth, Donikena Arya, K.V. Ciro Rodriguez, R. Lezama, Pedro Multimedia and Information Security Research Group Gwalior474015 India Department of Software Engineering Universidad Nacional Mayor de San Marcos UNMSM Lima Peru

ISBN: (纸本)9789819738588

The process of generating medical reports from medical images using deep learning techniques is a rapidly growing field. This task is critical and time-consuming for radiologists. Previous approaches focused on producing text that is readable for humans but may not represent the clinical facts well. Despite the difficulty of creating medical descriptions from images, deep learning algorithms show promising results. The earlier research primarily concentrated on generating lengthy descriptions of images but ignored the characteristics of the image and the important patient background information that can aid in diagnosis. To address these challenges, a creative encoder-decoder paradigm was proposed using an attention mechanism focused on chest X-ray images and reports. The attention mechanism is capable of simultaneously concentrating on the spatial and channel details of an image and linking it to the corresponding sentence topic. The IU X-ray dataset was used for study, which was collected from Indiana University. The proposed framework predicts medical descriptions based on chest X-ray images and creates a comprehensible radiology report. The suggested method learns how to extract significant image features from the deep convolutional neural network (CNN) model and generates textual descriptions. Our experiments have shown that the proposed model outperforms all other baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radiology

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FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference 38

FedSSP: Federated Graph Learning with Spectral Knowledge and...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Zihan Wan, Guancheng Huang, Wenke Ye, Mang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://***/OakleyTan/FedSSP. © 2024 Neural information processing systems foundation. All rights reserved.

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Predicting the Unseen: A Novel Dataset for Hidden Intention Localization in Pre-abnormal Analysis 24

Predicting the Unseen: A Novel Dataset for Hidden Intention ...

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32nd ACM International Conference on multimedia, MM 2024

作者： Qi, Zehao Zhang, Ruixu Hu, Xinyi Liu, Wenxuan Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798400706868

Our paper introduces a novel video dataset specifically for Temporal Intention Localization (TIL), aimed at identifying hidden abnormal intention in densely populated and complex environments. Traditional Temporal Action Localization (TAL) frameworks, focusing on overt actions within constrained temporal intervals, often miss subtle pre-abnormal actions that unfold over extended periods. Our dataset comprises 228 videos with 5790 clips, each annotated to capture fine-grained actions within ambiguous temporal boundaries using the Joint-Linear-Assignment methodology. This approach enables detailed analysis of the evolution of abnormal intention over time. To detect subtle, hidden intention, we developed the Intention-Action Fusion module, an creative approach integrating dynamic feature fusion across 11 behavioral subcategories, significantly enhancing the model's ability to discern nuanced intention. This enhancement has led to performance improvements of up to 139% in specific scenarios, dramatically boosting the model's sensitivity and interpretability, crucial for advancing proactive surveillance systems. By pushing the boundaries of technology, our dataset and methodologies foster proactive surveillance systems capable of preemptively identifying potential threats from nuanced behavioral patterns, encouraging further exploration into the complexities of intention beyond observable actions. The dataset is available at https://***/Zzz99999/Hidden-Abnormal-Intention. © 2024 ACM.

关键词： Security systems

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models 38

Reference Trustable Decoding: A Training-Free Augmentation P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Luohe Yao, Yao Li, Zuchao Zhang, Lefei Zhao, Hai National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without changing model parameters. However, this approach involves trade-offs, such as slower inference speed and increased space occupancy. PEFT assists the model in adapting to tasks through minimal parameter modifications, but the training process still demands high hardware requirements, even with a small number of parameters involved. To address these challenges, we propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning, maintaining low inference costs. RTD constructs a reference datastore from the provided training examples and optimizes the LLM's final vocabulary distribution by flexibly selecting suitable references based on the input, resulting in more trustable responses and enabling the model to adapt to downstream tasks at a low cost. Experimental evaluations on various LLMs using different benchmarks demonstrate that RTD establishes a new paradigm for augmenting models to downstream tasks. Furthermore, our method exhibits strong orthogonality with traditional methods, allowing for concurrent usage. Our code can be found at https://***/ShiLuohe/ReferenceTrustableDecoding. © 2024 Neural information processing systems foundation. All rights reserved.

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Expression Recognition and Intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network 5

Expression Recognition and Intelligent Classroom State Minin...

引用

5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Fan, Ruoyu Liang, Meiyu Yin, Mengran Du, Junping Multimedia School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software Beijing China

ISBN: (纸本)9798350393521

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset. © 2023 IEEE.

关键词： Students

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A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications 37

A Theory of Transfer-Based Black-Box Attacks: Explanation an...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Chen, Yanbo Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9781713899921

Transfer-based attacks [1] are a practical method of black-box adversarial attacks in which the attacker aims to craft adversarial examples from a source model that is transferable to the target model. Many empirical works [2-6] have tried to explain the transferability of adversarial examples from different angles. However, these works only provide ad hoc explanations without quantitative analyses. The theory behind transfer-based attacks remains a mystery. This paper studies transfer-based attacks under a unified theoretical framework. We propose an explanatory model, called the manifold attack model, that formalizes popular beliefs and explains the existing empirical results. Our model explains why adversarial examples are transferable even when the source model is inaccurate as observed in Papernot et al. [7]. Moreover, our model implies that the existence of transferable adversarial examples depends on the "curvature" of the data manifold, which further explains why the success rates of transfer-based attacks are hard to improve. We also discuss our model's expressive power and applicability. © 2023 Neural information processing systems foundation. All rights reserved.

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Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation 37

Adversarial Self-Training Improves Robustness and Generaliza...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Shi, Lianghe Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9781713899921

Gradual Domain Adaptation (GDA), in which the learner is provided with additional intermediate domains, has been theoretically and empirically studied in many contexts. Despite its vital role in security-critical scenarios, the adversarial robustness of the GDA model remains unexplored. In this paper, we adopt the effective gradual self-training method and replace vanilla self-training with adversarial self-training (AST). AST first predicts labels on the unlabeled data and then adversarially trains the model on the pseudo-labeled distribution. Intriguingly, we find that gradual AST improves not only adversarial accuracy but also clean accuracy on the target domain. We reveal that this is because adversarial training (AT) performs better than standard training when the pseudo-labels contain a portion of incorrect labels. Accordingly, we first present the generalization error bounds for gradual AST in a multiclass classification setting. We then use the optimal value of the Subset Sum Problem to bridge the standard error on a real distribution and the adversarial error on a pseudo-labeled distribution. The result indicates that AT may obtain a tighter bound than standard training on data with incorrect pseudo-labels. We further present an example of a conditional Gaussian distribution to provide more insights into why gradual AST can improve the clean accuracy for GDA. © 2023 Neural information processing systems foundation. All rights reserved.

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A Deep Understanding Video Q&A System for Film Education in Acting Department

A Deep Understanding Video Q&A System for Film Education in ...

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2023 International Conference on Intelligent Education and Intelligent Research, IEIR 2023

作者： Wu, Zhengqian Li, Ruizhe Guo, Jiahao Wang, Zhongyuan Liang, Chao Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science China National Engineering Research Center for Multimedia Software School of Computer Science China Wuhan University School of Computer Science China

ISBN: (纸本)9798350342895

Recently, advancements in artificial intelligence technology have greatly influenced the field of education, particularly in the area of intelligent homework assistance. However, current approaches are primarily designed for procedural and logical tasks and often lack comprehension abilities. This limitation is particularly evident when it comes to multi-hop and continuous tasks. To address this challenge, the integration of Large Language Model (LLM) has significantly enhanced the capability of AI systems to handle multi-hop and highly interconnected inputs. In this study, we focus on the learning needs of students in Acting Department, specifically their study of movies and the significance of classic movie videos in their learning process. However, assessing deep comprehension of classic movies poses its own challenges. To overcome these challenges, we develop a quiz system utilizing Knowledge Graphs (KG) and LLM to facilitate a deeper understanding of classic films. The generation of video quiz pairs is achieved through the use of Automatic Speech Recognition (ASR) technology, which leverages movie subtitles for question generation. For answering these questions, we employ techniques KG and LLM to process questions and retrieve corresponding answers. The proposed method achieves good performance in Deep Video Understanding (DVU) task of NIST TRECVID, demonstrating its effectiveness. © 2023 IEEE.

关键词： Motion pictures

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Cycle Self-Refinement for Multi-Source Domain Adaptation 38

Cycle Self-Refinement for Multi-Source Domain Adaptation

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Zhou, Chaoyang Wang, Zengmao Du, Bo Luo, Yong School of Computer Science Wuhan University China National Engineering Research Center for Multimedia Software Wuhan University China Institute of Artificial Intelligence Wuhan University China Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory China

ISBN: (纸本)1577358872

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Forecasting

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