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arXiv 2023年

作者： Zhou, Chulun Liang, Yunlong Meng, Fandong Zhou, Jie Xu, Jinan Wang, Hongji Zhang, Min Su, Jinsong School of Informatics Xiamen University Xiamen361005 China Beijing Jiaotong University Beijing100044 China Pattern Recognition Center WeChat AI Tencent Inc China Soochow University Suzhou215031 China School of Informatics Institute of Artificial Intelligence Xiamen University Xiamen361005 China Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage Fujian Taiwan Xiamen University Ministry of Culture and Tourism China Pengcheng Laboratory China

Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performances due to the following inherent problems: 1) limited resources of annotated bilingual dialogues;2) the neglect of modelling conversational properties;3) training discrepancy between different stages. To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues. We elaborately design two auxiliary tasks, namely utterance discrimination and speaker discrimination, to introduce the modelling of dialogue coherence and speaker characteristic into the NCT model. The training process consists of three stages: 1) sentence-level pre-training on large-scale parallel corpus;2) intermediate training with auxiliary tasks using additional monolingual dialogues;3) context-aware fine-tuning with gradual transition. Particularly, the second stage serves as an intermediate phase that alleviates the training discrepancy between the pre-training and fine-tuning stages. Moreover, to make the stage transition smoother, we train the NCT model using a gradual transition strategy, i.e., gradually transiting from using monolingual to bilingual dialogues. Extensive experiments on two language pairs demonstrate the effectiveness and superiority of our proposed training framework. © 2023, CC BY-NC-SA.

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Vehicle Routing Problem with Time Windows using Hybrid Metaheuristic Dragonfly Algorithm and Variable Neighborhood Search: Work on Progress

Vehicle Routing Problem with Time Windows using Hybrid Metah...

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Yunita Yunita Deris Stiawan Dian Palupi Rini Doctoral Student at Faculty of Computer Science Sriwijaya University Intelligent System Research Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera Communication Network and Information Security Research Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera Image Processing dan Pattern Recognition Laboratory Group Faculty of Computer Science Sriwijaya University Palembang South Sumatera

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

One of the problems with Smart Transportation is the problem of cost and travel time. This problem is known as the Variable Routing Problem (VRP). In some real cases, in addition to considering route selection, there are limitations on the capacity and time period for the vehicle to serve each customer known as Time Windows, so VRP has developed into Variable Routing Problem with Time Windows (VRPTW). One way to solve VRPTW is to use metaheuristic methods. However, the metaheuristic method has a weakness, which are it can get stuck in local optimums and fail to find better global solutions. To overcome this, a combination with other methods or known as hybrid is needed. The formulation of this research problem is how to develop a Smart transportation model using a metaheuristic hybrid algorithm. The purpose of this study is to develop a Smart transportation model using a metaheuristic hybrid algorithm. The method used is to combine two metaheuristic algorithms, which are the Dragonfly Algorithm (DA) and the Variable Neighborhood Search (VNS). The result of this research is a new algorithm model which is a hybrid between DA and VNS. Through a combination of global exploration using DA and local exploitation using VNS, this algorithm is expected to be able to find a better solution and faster convergence in solving VRPTW problems

关键词： Electrical engineering Computer science Costs Metaheuristics Vehicle routing Routing Search problems Smart transportation Informatics Convergence

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Semi-heterogeneous three-way joint embedding network for sketch-based image retrieval

arXiv

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arXiv 2019年

作者： Lei, Jianjun Song, Yuxin Peng, Bo Ma, Zhanyu Shao, Ling Song, Yi-Zhe School of Electrical and Information Engineering Tianjin University Tianjin300072 China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates SketchX Lab Centre for Vision Speech and Signal Processing University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

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MARL-based calibration for cameras with large FOV

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Optica Acta: International Journal of Optics 2024年第1-3期71卷

作者： Xinyu Yang Fuhan Chen Jingwen Jiang Hui Xie Qiaofeng Ou Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang People’s Republic of China

Achieving high-precision measurements in a large field of view (FOV) is a challenging task. The accuracy of vision measurements is determined by the quality of camera calibration, which is influenced by the pose of the target. To obtain suitable target pose, a target pose optimization method based on multi-agent reinforcement learning (MARL) is proposed. Firstly, the target pose optimization problem is modelled as a Markov decision process (MDP). Secondly, a multi-agent proximal policy optimization (MAPPO) algorithm for target pose optimization is designed by parameter sharing mechanism. Finally, optimization algorithm is adopted to camera calibration process. The calibration experiment was carried out under the large FOV of 4600 mm × 2300 mm. The results show that the back-projection error was 0.198 mm, the relative error of the diagonal length of target (505.847 mm) was 0.789 mm, and the success rate of large FOV camera calibration was 98.5%.

关键词： Camera calibration large FOV target pose optimization MARL MDP MAPPO

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Zero-Shot Audio Captioning Using Soft and Hard Prompts

arXiv

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arXiv 2024年

作者： Zhang, Yiming Xu, Xuenan Du, Ruoyi Liu, Haohe Dong, Yuan Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Electronic Systems Aalborg University Aalborg9220 Denmark The Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space. In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP. We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method. © 2024, CC BY.

关键词： Semantics

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Abnormal resting-state EEG power and impaired inhibition control in young smokers

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Neuroscience Letters 2021年第0期761卷 136120-136120页

作者： Dong, Fang Li, Xiaojian Zhang, Yunmiao Jia, Shaodi Zhang, Shidi Xue, Ting Ren, Yan Lv, Xiaoqi Yuan, Kai Yu, Dahua Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou 014010 Inner Mongolia China College of Information Engineering Inner Mongolia University of Technology Hohhot 010051 Inner Mongolia China School of Life Science and Technology Xidian University Xi'an 710071 Shaanxi China

Exposure to nicotine during adolescence may cause neurophysiological changes and increase the risks of developing nicotine dependence;it can even lead to lifelong smoking. The intake of nicotine may also lead to abnormal patterns of oscillatory brain activity and inhibition control deficits. However, little is known about the specific relationship between oscillatory brain activity during the resting state and inhibition control capacity in young smokers. In the present study, we acquired resting-state electroencephalography (EEG) data from thirty-four young smokers and 39 age-matched non-smoking controls. Inhibition control performance was measured by a Go/NoGo task. Compared with non-smoking controls, we detected reduced low-frequency delta band activity in the frontal, central and posterior cortices of young smokers. Furthermore, young smokers committed more errors in response to infrequent NoGo trials. Notably, we demonstrated that delta absolute power in the frontal region was negatively correlated with NoGo errors and that alpha power in the central region was positively correlated with NoGo errors in non-smoking controls but not in young smokers. These findings may suggest that these inhibitory control processes were associated with alterations in oscillatory brain activity during the resting state. Our findings suggest that alterations of power spectra in delta bands may act as a useful biomarker of inhibitory control performance and provide a scientific basis for the diagnosis and treatment of nicotine addiction in adolescents. © 2021 Elsevier B.V.

关键词： Brain oscillations Electroencephalography (EEG) Inhibition control Resting state Young smokers

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Affine Non-negative Collaborative Representation Based pattern Classification

arXiv

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arXiv 2020年

作者： Yin, He-Feng Wu, Xiao-Jun Feng, Zhen-Hua Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://***/yinhefeng/ANCR. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Oracle Character recognition by Nearest Neighbor Classification with Deep Metric Learning

Oracle Character Recognition by Nearest Neighbor Classificat...

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International Conference on Document Analysis and recognition

作者： Yi-Kang Zhang Heng Zhang Yong-Ge Liu Qing Yang Cheng-Lin Liu National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing P.R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P.R. China School of Computer & Information Engineering Anyang Normal University Henan P.R. China Ministry of Education Key Laboratory of Oracle Bone Inscriptions Information Processing Henan P.R. China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing P.R. China

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU.

关键词： Prototypes Character recognition Measurement Artificial neural networks Training Acceleration Network architecture

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Corrigendum to “FGPNet: A weakly supervised fine-grained 3D point clouds classification network” [pattern recognition 139 (2023) 109509]

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pattern recognition 2024年 151卷

作者： Huihui Shao Jing Bai Rusong Wu Jinzhe Jiang Hongbo Liang the School of Computer Science and Engineering North Minzu University Yinchuan 750021 China the Key Laboratory of Images Processing and Pattern Recognition Laboratory Commission: IPPRLab North Minzu University Yinchuan 750021 China

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