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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chang, Laibin Wang, Yunke Deng, Longxiang Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)157735897X

Underwater salient object detection (USOD) plays a pivotal role in various vision-based marine exploration tasks. However, existing USOD techniques face the dilemma of object mislocalization and imprecise boundaries due to the complex underwater environment. The quality degradation of raw underwater images (caused by selective absorption and medium scattering) makes it challenging to perform instance detection directly. One conceivable approach involves initially removing visual disturbances through underwater image enhancement (UIE), followed by saliency detection. However, this two-stage approach neglects the potential positive impact of the restoration procedure on saliency detection due to it executes in a cascade. Based on this insight, we propose a generalized prior-involved diffusion model, called WaterDiffusion for collaborative underwater saliency detection and visual restoration. Specifically, we first propose a revised self-attention joint diffusion, which embeds dynamic saliency masks into the diffusive network as latent features. By extending the underwater degradation prior into the multiscale decoder, we innovatively exploit optical transmission maps to aid in localizing underwater salient objects. Then, we further design a gate-guided binary indicator to select either normalized or raw channels for improving feature generalization. Finally, the Half-quadratic Splitting is introduced into the unfolding sampling to refine saliency masks iteratively. Comprehensive experiments demonstrate the superior performance of WaterDiffusion over state-of-the-art methods in both quantitative and qualitative evaluations. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Underwater photography

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A Multi-feature Co-distillation Algorithm Based on Human Skeletal Point Behavior Recognition 24

A Multi-feature Co-distillation Algorithm Based on Human Ske...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Pan, Xuefeng Tian, Zhiqiang School of Computer Wuhan Qingchuan University Wuhan China School of Information Wuhan Software Engineering Vocational College Wuhan Open University Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Depth model-based behavior estimation of human skeletal points is widely used in the field of behavior recognition. In order to improve the accuracy of behavior recognition, the complexity and computation of the designed network model are increasing, while the mobile platforms have limited arithmetic power and memory, and thus cannot be deployed. To solve the above problems, the AGCN network, a skeletal point-based behavior recognition model, is clipped to compress the network size so that it can support common mobile platforms;and a multi-feature co-distillation method is proposed to efficiently migrate the performance of the original network to the target network, so that the performance of the clipped lightweight network tends to be close to that of the original network. The model training, testing, visualization, and ablation experiments are completed on the NTU-RGB+D60 data set. The experimental results show that the algorithm after lightweighting (clipping network and multi-feature co-distillation), can significantly reduce the computational amount of behavior recognition. © 2024 Copyright held by the owner/author(s).

关键词： Distillation

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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A Fine-Grained Detector of Face Mask Wearing Status Based on Improved YOLOX

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1816-1830页

作者： Xiao, Hongli Wang, Bingshu Zheng, Jiangbin Liu, Licheng Philip Chen, C.L. Northwestern Polytechnical University School of Software Taicang Campus Suzhou215400 China Shanghai Jiaotong University School of Electronic Information and Electrical Engineering Shanghai200240 China Hunan University College of Electrical and Information Engineering Changsha410082 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

The fast outbreak of coronavirus disease 2019 (COVID-19) and rapid proliferation of its variants have continued to pose a huge challenge to people around the world. Wearing medical masks properly in public and private settings can protect people from COVID-19, which brings a growing demand for automatic detection services of face mask wearing conditions. In this article, we propose a fine-grained detector called ECA_YOLOX-S to identify the wearing status of face masks. Efficient channel attention is introduced into YOLOX-S to reach a tradeoff between effectiveness and efficiency. To demonstrate the performance of our proposed method, a Fine-grained Face Mask (FineFM) dataset is created, which covers four classes of mask wearing status. The proposed FineFM dataset has 16 955 annotated images and covers multiple realistic scenarios. To the best of authors' knowledge, it has the largest number of improper mask wearing images among all similar datasets for realistic scenes. Experiments conducted on the FineFM dataset demonstrate that ECA_YOLOX-S achieves an overall mean average precision (mAP)@.50:95 of 86.80% for moderate scenes and an overall mAP@.50:95 of 73.20% for complex scenes, outperforming its benchmark model. Moreover, experiments conducted on other realistic and simulated datasets indicate that the proposed detector has advantages over other methods. © 2020 IEEE.

关键词： COVID-19

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Aspect-Based Sentiment Analysis Based on Knowledge Enhancement and Pre-trained Language Model

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 1168-1181页

作者： Ye, Lizhu Johar, Md Gapar Md Alkawaz, Mohammed Hazim School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University Selangor Shah Alam40100 Malaysia Software Engineering and Digital Innovation Center Management and Science University Selangor Shah Alam40100 Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq

Aspect-Based Sentiment Analysis (ABSA) is a significant task in the field of natural language processing, and the existing ABSA models not only lack the mining of sentiment knowledge, but also ignore the semantic feature representation of text. To address the above issues, this article designs an ABSA method relied on knowledge enhancement and Pre-trained Language Model (PLM). Firstly, a syntactic dependency graph is constructed based on the sentiment knowledge of the text to integrate the syntactic and sentiment information in the sentence. On this basis, PLM is utilized to encode words and sentences in the text context to enrich the semantic feature representation of the text. Secondly, the dependency graph of the sentence is enhanced by using emotional knowledge and the dependency types between words, and the node features are modeled by graph convolutional network to get the specific aspectual representation enhanced by emotional knowledge;meanwhile, the conceptual graph is enhanced by using conceptual graph for the aspectual word concepts and then fused with the specific aspectual representation to get the aspectual representation enhanced by multi-source knowledge. Then designing a dual interaction attention mechanism to realize the fusion of context and aspect features. Finally, the fused characteristic information is adopted as the input of the fully connected layer, and the softmax function is used to output the final sentiment polarity classification. The experimental outcome indicates that the suggested method outperforms the comparison model on the Restaurant and Twitter datasets, and better experimental outcome are obtained. © 2025 ISSN.

关键词： Emotion Recognition

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Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification

Message Passing on Semantic-Anchor-Graphs for Fine-grained E...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Pinyi Chen, Jingyang Shen, Junchen Zhai, Zijie Li, Ping Zhang, Jie Zhang, Kai School of Computer Science and Technology East China Normal University China Institute of Science and Technology for Brain-inspired intelligence Fudan University China School of Computer Science and Software Engineering Southwest Petroleum University China

ISBN: (纸本)9798891761643

Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture their complex semantic and temporal distributions. To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification. It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as global reference, any sentence can be projected onto them to form a "semantic-anchor graph", with node attributes and edge weights quantifying semantic and temporal information, respectively. The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding K different anchors. Message passing on the anchor graph can further integrate the semantic and temporal information and refine the learned features. Empirically, SEAN-GNN produces meaningful semantic anchors and discriminative graph patterns, with promising classification results on 6 popular benchmark datasets against state-of-the-arts. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Causal Label Enhancement

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 3933-3946页

作者： Liu, Xinyuan Zhu, Jihua Zheng, Qinghai Li, Zhongyu Tian, Zhiqiang Zhu, Mingchen Xi’an Jiaotong University School of Software Engineering Xi’an710049 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210008 China Fuzhou University College of Computer and Data Science Fuzhou350025 China University of California DavisCA95616 United States

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.

关键词： Contrastive Learning

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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An Air Quality Oriented Convolutional and Interaction Network for Traffic Flow Prediction in VANET 16

An Air Quality Oriented Convolutional and Interaction Networ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Cheng, Hanlin Li, Xuan Zhou, Tianqing Yao, Yu Wang, Kan Zhou, Momiao School of Information and software Engineering East China Jiaotong University Nanchang China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350390643

To accurately predict traffic flow, it is necessary to take into account not only the volume of road traffic, but also external urban factors. Particularly, the correlations between urban air quality and traffic flow is particularly strong, However, due to the complexity of air composition and its nonlinear characteristics in spatial and temporal sequences, it remains underexplored in current research efforts. In this paper, we propose an air quality oriented convolutional and interaction network for traffic flow prediction in Vehicular ad-hoc network, AQCInet, which captures feature information from both types of data using convolutional block, integrates this feature information with interaction networks, and effectively represents the long-term and short-term dependencies in the sequential data, ulti-mately outputting short-term traffic flow prediction results. The experimental results show that AQCInet can effectively model traffic flow sequences with complex time dynamics by aggregating rich features from multiple resolutions. Compared with existing model, our proposed model significantly improves RMSE, MSE, and MAE through jointly considerations of the relationship between air quality, various air component concentrations, and traffic flow. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Recoverable Facial Identity Protection via Adaptive Makeup Transfer Adversarial Attacks 39

Recoverable Facial Identity Protection via Adaptive Makeup T...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Xiyao Ma, Junxing Wang, Xinda Lin, Qianyu Zhang, Jian Schaefer, Gerald Turkay, Cagatay Fang, Hui School of Computer Science and Engineering Central South University China School of Software and Microelectronics Peking University China Department of Computer Science Loughborough University United Kingdom Centre for Interdisciplinary Methodologies University of Warwick United Kingdom

ISBN: (纸本)157735897X

Unauthorised face recognition (FR) systems have posed significant threats to digital identity and privacy protection. To alleviate the risk of compromised identities, recent makeup transfer-based attack methods embed adversarial signals in order to confuse unauthorised FR systems. However, their major weakness is that they set up a fixed image unrelated to both the protected and the makeup reference images as the confusion identity, which in turn has a negative impact on both attack success rate and visual quality of transferred photos. In addition, the generated images cannot be recognised by authorised FR systems once attacks are triggered. To address these challenges, in this paper, we propose a Recoverable Makeup Transferred Generative Adversarial Network (RMT-GAN) which has the distinctive feature of improving its image-transfer quality by selecting a suitable transfer reference photo as the target identity. Moreover, our method offers a solution to recover the protected photos to their original counterparts that can be recognised by authorised systems. Experimental results demonstrate that our method provides significantly improved attack success rates while maintaining higher visual quality compared to state-of-the-art makeup transfer-based adversarial attack methods. Our code and supplementary materials are available on Github. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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