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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation

arXiv

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arXiv 2024年

作者： Ma, Zhengrui Fang, Qingkai Zhang, Shaolei Guo, Shoutao Feng, Yang Zhang, Min Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

Simultaneous translation models play a crucial role in facilitating communication. However, existing research primarily focuses on text-to-text or speech-to-text models, necessitating additional cascade components to achieve speech-to-speech translation. These pipeline methods suffer from error propagation and accumulate delays in each cascade component, resulting in reduced synchronization between the speaker and listener. To overcome these challenges, we propose a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2x1), which integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework. We develop a non-autoregressive decoder capable of concurrently generating multiple text or acoustic unit tokens upon receiving fixed-length speech chunks. The decoder can generate blank or repeated tokens and employ CTC decoding to dynamically adjust its latency. Experimental results show that NAST-S2x outperforms state-of-the-art models in both speech-to-text and speech-to-speech tasks. It achieves high-quality simultaneous interpretation within a delay of less than 3 seconds and provides a 28× decoding speedup in offline generation. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks 19

Graph Regularized Nonnegative Latent Factor Analysis Model f...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zhou, Yue Liu, ZhiGang Yuan, Ye Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Southwest University College of Computer and Information Science Chongqing400715 China

ISBN: (纸本)9781665472432

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency. © 2022 IEEE.

关键词： Forecasting

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Exploiting Robust Model Watermarking Against the Model Fine-Tuning Attack via Flat Minima Aware Optimizers

Exploiting Robust Model Watermarking Against the Model Fine-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dongdong Lin Yue Li Bin Li Jiwu Huang Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep neural networks (DNNs), model watermarking has emerged as a widely adopted technique for safeguarding model copyrights. A prevalent method involves utilizing a watermark decoder to retrieve watermark bits from generated outputs, but such methods are often vulnerable to model fine-tuning attacks. Traditionally, this challenge is mitigated through adversarial training or data augmentation, both of which significantly increase the computational burden. In this paper, we present a solution employing Flat Minima Aware (FMA) optimizers to bolster the robustness of model watermarking without requiring additional training data. By optimizing the watermark loss with flat minima awareness, our approaches significantly enhance the robustness of watermarks against the model fine-tuning attack. Comprehensive experiments have demonstrated our method’s superior ability to preserve watermark integrity. These findings suggest that this innovative optimization strategy offers a robust and efficient pathway for protecting models, thereby contributing to more secure and reliable model copyright protection mechanisms.

关键词： Training Resistance Computational modeling Training data Watermarking Signal processing Diffusion models Robustness Speech processing Optimization

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MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation

arXiv

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arXiv 2024年

作者： Huang, Langlin Bu, Mengyu Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences China Washington University St. Louis United States

Byte-based machine translation systems have shown significant potential in massively multilingual settings. Unicode encoding, which maps each character to specific byte(s), eliminates the emergence of unknown words, even in new languages. This avoids out-of-vocabulary risk in multilingual translation and enables broad language scalability. However, byte-level tokenization results in sequences that are hard to interpret due to limited semantic information per byte. Local contextualization has proven effective in assigning initial semantics to tokens, improving sentence comprehension. Nevertheless, variations in encoding rules across languages necessitate an adaptive approach for effective contextualization. To this end, we propose Mixture of Contextualization Experts (MoCE), adaptively selecting and mixing attention heads, which are treated as contextualization experts. This enhances the flexibility of contextualization scales and allows models to search for better contextualization combinations. Experiment results show that our method outperforms existing methods without extensive manual adjustment of hyper-parameters and surpasses subword-based models with fewer parameters in Ted-59 dataset. Our code is available at https://***/ictnlp/MoCE. © 2024, CC BY-NC-ND.

关键词： Encoding (symbols)

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A Grouping Strategy-Based Progressive Fusion Network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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An Improved Differential Evolution Algorithm Integrating the Plant Evolutionary Strategy

SSRN

引用

SSRN 2024年

作者： Zhang, Xiaoming Cheng, Hongwei Li, Jun Li, Tingjuan Zhang, Panpan Tian, Ye Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Qinghai Institute of Science and Technology Information Xining810008 China School of Computer Science and Technology Anhui University Hefei230601 China

Differential evolution algorithm (DE) and their variants often encounter a decrease in population diversity and convergence rate along with the evolution, resulting in local optima and premature convergence. To address this issue, this paper proposes an improved differential evolution integrating the plant evolutionary strategy, named DEPES, leveraging the exploitation capability of DE and the exploration capability of the plant population distribution evolutionary strategy. First, a tri-subpopulation optimization framework with a mutation strategy is introduced to cope with diversity maintenance, convergence enhancement, and local optima a Cauchy mutation operator based on plant population distribution evolutionary model is employed to enhance the exploration capability of the mutation strategy. Second, a parameter control strategy is performed on the superior and inferior solutions to balance the exploitation and exploration of the population. Third, a covariance matrix-based crossover strategy is designed to minimize interference from local population information, consequently generating high-quality solutions. The superior efficiency of DEPES is fully verified by comparing it with advanced DE-based variants and other algorithms on the IEEE CEC2020 test suite and engineering design problems. © 2024, The Authors. All rights reserved.

关键词： Covariance matrix

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Learning to Learn Transferable Generative Attack for Person Re-Identification

arXiv

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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Auto Encoding Neural Process for Multi-interest Recommendation 39

Auto Encoding Neural Process for Multi-interest Recommendati...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiang, Yiheng Xu, Yuanbo Yang, Yongjian Yang, Funing Wang, Pengyang Li, Chaozhuo Lab of Mobile Intelligent Computing College of Computer Science and Technology Jilin University China Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau Macao Beijing University of Aeronautics and Astronautics China

ISBN: (纸本)157735897X

Multi-interest recommendation constantly aspires to an oracle individual preference modeling approach, one that satisfies the diverse and dynamic properties. Fueled by deep learning technologies, existing neural network (NN)-based recommender systems employ single-point or multi-point interest representation strategies for preference modeling, and boost the recommendation performance with remarkable margins. However, as the parameterized approximate function nature, NN-based methods remain deficiencies regarding the adaptability towards distinctive preference patterns cross different users, and the calibration over individual current intents. In this paper, we revisit multi-interest recommendation with the lens of stochastic process and Bayesian inference. Specifically, we propose to learn a distribution over functions to depict the individual diverse preferences rather than a unified function to approximate preference. Subsequently, the recommendation is equipped with the uncertainty estimation which conforms to the dynamic shifting intent. Along these lines, we establish the connection between multi-interest recommendation and neural processes by proposing NP-Rec, which realizes the flexible multiple interests modeling and uncertainty estimation, simultaneously. Empirical study on 4 real world datasets demonstrates that our NP-Rec attains superior recommendation performances to several state-of-the-art baselines, where the average improvement achieves up to 13.94%. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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