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Review of Deep Learning Applied to Time Series Prediction LIANG Hongtao, LIU Shuo, DU Junwei, HU Qiang, YU Xu Journal of Frontiers of Computer Science and Technology    2023, 17 (6): 1285-1300.   DOI: 10.3778/j.issn.1673-9418.2211108 The time series is generally a set of random variables that are observed and collected at a certain frequency in the course of something??s development. The task of time series forecasting is to extract the core patterns from a large amount of data and to make accurate estimates of future data based on known factors. Due to the access of a large number of IoT data collection devices, the explosive growth of multidimensional data and the increasingly demanding requirements for prediction accuracy, it is difficult for classical parametric models and traditional machine learning algorithms to meet high efficiency and high accuracy requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Trans-former models have achieved fruitful results in time series forecasting tasks. To further promote the development of time series prediction technology, common characteristics of time series data, evaluation indexes of datasets and models are reviewed, and the characteristics, advantages and limitations of each prediction algorithm are experimentally compared and analyzed with time and algorithm architecture as the main research line. Several time series prediction methods based on Transformer model are highlighted and compared. Finally, according to the problems and challenges of deep learning applied to time series prediction tasks, this paper provides an outlook on the future research trends in this direction. Reference | Related Articles | Metrics

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Research on Question Answering System on Joint of Knowledge Graph and Large Language Models ZHANG Heyi, WANG Xin, HAN Lifan, LI Zhao, CHEN Zirui, CHEN Zhe Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2377-2388.   DOI: 10.3778/j.issn.1673-9418.2308070 The large language model (LLM), including ChatGPT, has shown outstanding performance in understanding and responding to human instructions, and has a profound impact on natural language question answering (Q&A). However, due to the lack of training in the vertical field, the performance of LLM in the vertical field is not ideal. In addition, due to its high hardware requirements, training and deploying LLM remains difficult. In order to address these challenges, this paper takes the application of traditional Chinese medicine formulas as an example, collects the domain related data and preprocesses the data. Based on LLM and knowledge graph, a vertical domain Q&A system is designed. The system has the following capabilities: (1) Information filtering. Filter out vertical domain related questions and input them into LLM to answer. (2) Professional Q&A. Generate answers with more professional knowledge based on LLM and self-built knowledge base. Compared with the fine-tuning method of introducing professional data, using this technology can deploy large vertical domain models without the need for retraining. (3) Extract conversion. By strengthening the information extraction ability of LLM and utilizing its generated natural language responses, structured knowledge is extracted and matched with a professional knowledge graph for professional verification. At the same time, structured knowledge can be transformed into readable natural language, achieving a deep integration of large models and knowledge graphs. Finally, the effect of the system is demonstrated and the performance of the system is verified from both subjective and objective perspectives through two experiments of subjective evaluation of experts and objective evaluation of multiple choice questions. Reference | Related Articles | Metrics

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Survey on Sequence Data Augmentation GE Yizhou, XU Xiang, YANG Suorong, ZHOU Qing, SHEN Furao Journal of Frontiers of Computer Science and Technology    2021, 15 (7): 1207-1219.   DOI: 10.3778/j.issn.1673-9418.2012062 To pursue higher accuracy, the structure of deep learning model is getting more and more complex, with deeper and deeper network. The increase in the number of parameters means that more data are needed to train the model. However, manually labeling data is costly, and it is not easy to collect data in some specific fields limited by objective reasons. As a result, data insufficiency is a very common problem. Data augmentation is here to alleviate the problem by artificially generating new data. The success of data augmentation in the field of computer vision leads people to consider using similar methods on sequence data. In this paper, not only the time-domain methods such as flipping and cropping but also some augmentation methods in frequency domain are described. In addition to experience-based or knowledge-based methods, detailed descriptions on machine learning models used for automatic data generation such as GAN are also included. Methods that have been widely applied to various sequence data such as text, audio and time series are mentioned with their satisfactory performance in issues like medical diagnosis and emotion classification. Despite the difference in data type, these methods are designed with similar ideas. Using these ideas as a clue, various data augmentation methods applied to different types of sequence data are introduced, and some discussions and prospects are made. Reference | Related Articles | Metrics

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Summary of Multi-modal Sentiment Analysis Technology LIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie Journal of Frontiers of Computer Science and Technology    2021, 15 (7): 1165-1182.   DOI: 10.3778/j.issn.1673-9418.2012075 Sentiment analysis refers to the use of computers to automatically analyze and determine the emotions that people want to express. It can play a significant role in human-computer interaction and criminal investigation and solving cases. The advancement of deep learning and traditional feature extraction algorithms provides conditions for the use of multiple modalities for sentiment analysis. Combining multiple modalities for sentiment analysis can make up for the instability and limitations of single-modal sentiment analysis, and can effectively improve accuracy. In recent years, researchers have used three modalities of facial expression information, text information, and voice information to perform sentiment analysis. This paper mainly summarizes the multi-modal sentiment analysis technology from these three modalities. Firstly, it briefly introduces the basic concepts and research status of multi-modal sentiment analysis. Secondly, it summarizes the commonly used multi-modal sentiment analysis datasets. It gives a brief description of the existing single-modal emotion analysis technology based on facial expression information, text information and voice information. Next, the modal fusion technology is introduced in detail, and the existing results of the multi-modal sentiment analysis technology are mainly described according to different modal fusion methods. Finally, it discusses the problems of multi-modal sentiment analysis and future development direction. Reference | Related Articles | Metrics

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Survey of Research on Deep Learning Image-Text Cross-Modal Retrieval LIU Ying, GUO Yingying, FANG Jie, FAN Jiulun, HAO Yu, LIU Jiming Journal of Frontiers of Computer Science and Technology    2022, 16 (3): 489-511.   DOI: 10.3778/j.issn.1673-9418.2107076 As the rapid development of deep neural networks, multi-modal learning techniques are widely concerned. Cross-modal retrieval is an important branch of multimodal learning. Its fundamental purpose is to reveal the relation between different modal samples by retrieving modal samples with identical semantics. In recent years, cross-modal retrieval has gradually become the forefront and hot spot of academic research. It’s an important direction in the future development of information retrieval. This paper focuses on the latest development of cross-modal retrieval based on deep learning, reviews the development trends of real value representation-based and binary representation-based learning methods systematically. Among them, the real value representation-based method is adopted to improve the semantic relevance, and improve the accuracy, and the binary representation-based learning method is used to improve the efficiency of image-text cross-modal retrieval and reduce storage space. In addition, the common open datasets in the field of image-text cross-modal retrieval are summarized, and the performance of various algorithms on different datasets is compared. Especially, this paper summarizes and analyzes the specified implementations of cross-modal retrieval techniques in the fields of public security, media and medicine. Finally, combined with the state-of-the-art technologies, development trends and future research directions are discussed. Table and Figures | Reference | Related Articles | Metrics

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Review of Pre-training Techniques for Natural Language Processing CHEN Deguang, MA Jinlin, MA Ziping, ZHOU Jie Journal of Frontiers of Computer Science and Technology    2021, 15 (8): 1359-1389.   DOI: 10.3778/j.issn.1673-9418.2012109 In the published reviews of natural language pre-training technology, most literatures only elaborate neural network pre-training technologies or a brief introduction to traditional pre-training technologies, which may result in the development process of natural language pre-training dissected artificially from natural language processing. Therefore, in order to avoid this phenomenon, this paper covers the process of natural language pre-training with four points as follows. Firstly, the traditional natural language pre-training technologies and neural network pre-training technologies are introduced according to the updating route of pre-training technology. With the characteristics of related technologies analyzed, compared, this paper sums up the process of development context and trend of natural language processing technology. Secondly, based on the improved BERT (bidirectional encoder representation from transformers), this paper mainly introduces the latest natural language processing models from two aspects and sums up these models from pre-training mechanism, advantages and disadvantages, performance and so on. The main application fields of natural language processing are presented. Furthermore, this paper explores the challenges and corresponding solutions to natural language processing models. Finally, this paper summarizes the work of this paper and prospects the future development direction, which can help researchers understand the development of pre-training technologies of natural language more comprehensively and provide some ideas to design new models and new pre-training methods. Reference | Related Articles | Metrics

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Survey of Chinese Named Entity Recognition ZHAO Shan, LUO Rui, CAI Zhiping Journal of Frontiers of Computer Science and Technology    2022, 16 (2): 296-304.   DOI: 10.3778/j.issn.1673-9418.2107031 The Chinese named entity recognition (NER) task is a sub-task within the information extraction domain, where the task goal is to find, identify and classify relevant entities, such as names of people, places and organizations, from sentences given a piece of unstructured text. Chinese named entity recognition is a fundamental task in the field of natural language processing (NLP) and plays an important role in many downstream NLP tasks, including information retrieval, relationship extraction and question and answer systems. This paper provides a comprehensive review of existing neural network-based word-character lattice structures for Chinese NER models. Firstly, this paper introduces that Chinese NER is more difficult than English NER, and there are difficulties and challenges such as difficulty in determining the boundaries of Chinese text-related entities and complex Chinese grammatical structures. Secondly, this paper investigates the most representative lattice-structured Chinese NER models under different neural network architectures (RNN (recurrent neural network), CNN (convolutional neural network), GNN (graph neural network) and Transformer). Since word sequence information can capture more boundary information for character-based sequence learning, in order to explicitly exploit the lexical information associated with each character, some prior work has proposed integrating word information into character sequences via word-character lattice structures. These neural network-based word-character lattice structures perform significantly better than word-based or character-based approaches on the Chinese NER task. Finally, this paper introduces the dataset and evaluation criteria of Chinese NER. Table and Figures | Reference | Related Articles | Metrics

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Survey of Affective-Based Dialogue System ZHUANG Yin, LIU Zhen, LIU Tingting, WANG Yuanyi, LIU Cuijuan, CHAI Yanjie Journal of Frontiers of Computer Science and Technology    2021, 15 (5): 825-837.   DOI: 10.3778/j.issn.1673-9418.2012012 As an important way of human-computer interaction, the dialogue system has broad application prospects. Existing dialogue systems focus on solving problems such as semantic consistency and content richness and paying little attention to improving human-computer interaction and human-computer resonance. How to make the generated sentences communicate with users more naturally on the basis of semantic relevance is one of the main problems in current dialogue system. First, it summarizes the overall situation of the dialogue system. Then it introduces the two major tasks of dialogue emotion perception and emotional dialogue generation in the emotional dialogue system. And further it investigates and summarizes related methods respectively. Dialogue emotion perception tasks are roughly divided into context-based and user-based methods. The emotional dialogue generation methods include rule matching algorithms, specified emotional response generation models, and non-specified emotional response generation models. The models are compared and analyzed in terms of emotional data categories and model methods. Next, for subsequent research, it summarizes characteristics and links of the data sets under the two major tasks. Further, different evaluation methods in the current emotional dialogue system are summarized. Finally, the work of the emotional dialogue system is summarized and prospected. Reference | Related Articles | Metrics

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Survey of Video Object Detection Based on Deep Learning WANG Dicong, BAI Chenshuai, WU Kaijun Journal of Frontiers of Computer Science and Technology    2021, 15 (9): 1563-1577.   DOI: 10.3778/j.issn.1673-9418.2103107 Video object detection is to solve the problem of object localization and recognition in every video frame. Compared with image object detection, video is featured by high redundancy, which contains a lot of local spatio-temporal information. With the rapid popularity of deep convolutional neural network in the field of static image object detection, it shows a great advantage over traditional methods in performance. Besides, it plays a due role in video-based object detection task. However, the current video object detection algorithms still face many challenges, such as improving and optimizing the performance of mainstream object detection algorithms, maintaining the spatiotemporal consistency of video sequences, and making detection of model lightweight. In view of the above problems and challenges, on the basis of investigating a large number of literature, this paper systematically sum-marizes the video object detection algorithm based on deep learning. Based on the basic methods like optical flow and detection, these algorithms are classified. In addition, in the angles of backbone network, algorithm structure and data sets etc., these methods are explored. Combined with the experimental results in the ImageNet VID data set, this paper analyzes the performance advantages and disadvantages of typical algorithms of this field, and the relations between these algorithms. As for video object detection, the problems to be solved as well as the future research direction are expounded and prospected. Video object detection has become a hot spot pursued by many computer vision scholars. More efficient and accurate algorithms will be proposed, and its development direction will be better and better. Reference | Related Articles | Metrics

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Survey of Open-Domain Knowledge Graph Question Answering CHEN Zirui, WANG Xin, WANG Lin, XU Dawei, JIA Yongzhe Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1843-1869.   DOI: 10.3778/j.issn.1673-9418.2106095 Knowledge graph question answering (KGQA) is the procedure of processing natural language questions posed by users to obtain relevant answers from knowledge graph (KG) based on some form of KG. Due to the limitation of knowledge scale, computing power and natural language processing capability, the early knowledge base question answering systems were limited to closed-domain questions. In recent years, with the development of KG and the construction of open-domain question answering (QA) datasets, KG has been used for open-domain QA research and practice. In this paper, in accordance with the development of technology, the open-domain KGQA is summarized. Firstly, five rule and template based KGQA methods are reviewed, including traditional semantic parsing, traditional information retrieval, triplet matching, utterance template, and query template. This type of methods mainly relies on manually defined rules and templates to complete QA task. Secondly, five deep learning based KGQA methods are introduced, which use neural network models to complete the subtasks of QA process, including knowledge graph embedding, memory network, neural network-based semantic parsing, neural network-based query graph, and neural network-based information retrieval method. Thirdly, four general domain KG and eleven open-domain QA datasets, which KGQA commonly used are described. Fourthly, three classic KGQA datasets are selected according to the difficulty of questions to compare and analyze the performance metric of each KGQA system, and the effect between above methods. Finally, this paper looks forward to the future research directions on this topic. Reference | Related Articles | Metrics

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Review of Semi-supervised Deep Learning Image Classification Methods LYU Haoyuan, YU Lu, ZHOU Xingyu, DENG Xiang Journal of Frontiers of Computer Science and Technology    2021, 15 (6): 1038-1048.   DOI: 10.3778/j.issn.1673-9418.2011020 As one of the most concerned technologies in the field of artificial intelligence in recent ten years, deep learning has achieved excellent results in many applications, but the current learning strategies rely heavily on a large number of labeled data. In many practical problems, it is not feasible to obtain a large number of labeled training data, so it increases the training difficulty of the model. But it is easy to obtain a large number of unlabeled data. Semi-supervised learning makes full use of unlabeled data, provides solutions and effective methods to improve the performance of the model under the condition of limited labeled data, and achieves high recognition accuracy in the task of image classification. This paper first gives an overview of semi-supervised learning, and then introduces the basic ideas commonly used in classification algorithms. It focuses on the comprehensive review of image classification methods based on semi-supervised deep learning framework in recent years, including multi- view training, consistency regularization, diversity mixing and semi-supervised generative adversarial networks. It summarizes the common technologies of various methods, analyzes and compares the differences of experimental results of different methods. Finally, this paper thinks about the existing problems and looks forward to the feasible research direction in the future. Reference | Related Articles | Metrics

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Review of Knowledge Distillation in Convolutional Neural Network Compression MENG Xianfa, LIU Fang, LI Guang, HUANG Mengmeng Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1812-1829.   DOI: 10.3778/j.issn.1673-9418.2104022 In recent years, convolutional neural network (CNN) has made remarkable achievements in many applications in the field of image analysis with its powerful ability of feature extraction and expression. However, the continuous improvement of CNN performance is almost entirely due to the deeper and larger network model. In this case, the deployment of a complete CNN often requires huge memory overhead and high-performance computing units (such as GPU) support. However, there are limitations in the wide application of CNN in embedded devices with limited computing resources and mobile terminals with high real-time requirements. Therefore, CNN urgently needs network lightweight. At present, the main ways to solve the above problems are knowledge distillation, network pruning, parameter quantization, low rank decomposition, lightweight network design, etc. This paper first introduces the basic structure and development process of convolutional neural network, and briefly describes and compares five typical basic methods of network compression. Then, the knowledge distillation methods are combed and summarized in detail, and the different methods are compared experimentally on the CIFAR data set. Furthermore, the current evaluation system of knowledge distillation methods is introduced. The comparative analysis and evaluation of many types of methods are given. Finally, the preliminary thinking on the future development of this technology is given. Reference | Related Articles | Metrics

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Survey of One-Stage Small Object Detection Methods in Deep Learning LI Kecen, WANG Xiaoqiang, LIN Hao, LI Leixiao, YANG Yanyan, MENG Chuang, GAO Jing Journal of Frontiers of Computer Science and Technology    2022, 16 (1): 41-58.   DOI: 10.3778/j.issn.1673-9418.2110003 With the development of deep learning, object detection technology has gradually changed from traditional manual detection methods to deep neural network detection methods. Among many object detection algorithms based on deep learning, the one-stage object detection method based on deep learning is widely used because of its simple network structure, fast running speed and higher detection efficiency. However, the existing one-stage object detection methods based on deep learning do not have ideal detection results for small target objects in the detection process due to the lack of feature information, low resolution, complicated background information, unobvious details and higher positioning accuracy, which reduces the detection accuracy of the model. Aiming at the existing problems of one-stage object detection method based on deep learning, a large amount of one-stage small object detection technologies based on deep learning are studied. Firstly, the optimization methods for small object detection are systematically summarized from the aspects of Anchor Box, network structure, IoU (intersection over union) and loss function in the one-stage object detection methods. Secondly, the commonly used small object detection datasets and their application fields are listed, and the detection graphs on each small object detection dataset are given. Finally, the future research direction of one-stage small object detection methods based on deep learning is investigated. Table and Figures | Reference | Related Articles | Metrics

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Survey on Deep Learning Based News Recommendation Algorithm TIAN Xuan, DING Qi, LIAO Zihui, SUN Guodong Journal of Frontiers of Computer Science and Technology    2021, 15 (6): 971-998.   DOI: 10.3778/j.issn.1673-9418.2007021 News recommendation (NR) can effectively alleviate the overload of news information, and it is an important way to obtain news information for users. Deep learning (DL) has become a mainstream technology to promote the development of NR in recent years, and the effect of news recommendation has been significantly improved, which is widely concerned by researchers. In this paper, the methods of deep learning-based news recommendation (DNR) are classified, analyzed and summarized. In the research of NR, modeling users or news are two key tasks. According to different strategies of modeling users or news, the news recommendation methods based on deep learning are divided into three types: “two-stage” method, “fusion” method and “collaboration” method. Each type of method is further subdivided in terms of sub-tasks or the data organization structure based on. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The characteristics, advantages and disadvantages of each type of methods are also summarized in detail. Furthermore, the commonly used datasets, baseline and performance evaluation indicators are introduced. Finally, the possible future research directions and development trends in this field are analyzed and predicted. Reference | Related Articles | Metrics

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Review on Named Entity Recognition LI Dongmei, LUO Sisi, ZHANG Xiaoping, XU Fu Journal of Frontiers of Computer Science and Technology    2022, 16 (9): 1954-1968.   DOI: 10.3778/j.issn.1673-9418.2112109 In the field of natural language processing, named entity recognition is the first key step of information extraction. Named entity recognition task aims to recognize named entities from a large number of unstructured texts and classify them into predefined types. Named entity recognition provides basic support for many natural language processing tasks such as relationship extraction, text summarization, machine translation, etc. This paper first introduces the definition of named entity recognition, research difficulties, particularity of Chinese named entity recognition, and summarizes the common Chinese and English public datasets and evaluation criteria in named entity recognition tasks. Then, according to the development history of named entity recognition, the existing named entity recognition methods are investigated, which are the early named entity recognition methods based on rules and dictionaries, the named entity recognition methods based on statistic and machine learning, and the named entity recognition methods based on deep learning. This paper summarizes the key ideas, advantages and disadvan-tages and representative models of each named entity recognition method, and summarizes the Chinese named entity recognition methods in each stage. In particular, the latest named entity recognition based on Transformer and based on prompt learning are reviewed, which are state-of-the-art in deep learning-based named entity recognition methods. Finally, the challenges and future research trends of named entity recognition are discussed. Table and Figures | Reference | Related Articles | Metrics

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Survey of Few-Shot Object Detection LIU Chunlei, CHEN Tian‘en, WANG Cong, JIANG Shuwen, CHEN Dong Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 53-73.   DOI: 10.3778/j.issn.1673-9418.2206020 Object detection as a hot field in computer vision, usually requires a large number of labeled images for model training, which will cost a lot of manpower and material resources. At the same time, due to the inherent long-tailed distribution of data in the real world, the number of samples of most objects is relatively small, such as many uncommon diseases, etc., and it is difficult to obtain a large number of labeled images. In this regard, few-shot object detection only needs to provide a small amount of annotation information to detect objects of interest. This paper makes a detailed review of few-shot object detection methods. Firstly, the development of general target detection and its existing problems are reviewed, the concept of few-shot object detection is introduced, and other tasks related to few-shot object detection are differentiated and explained. Then, two classical paradigms based on transfer learning and meta-learning for existing few-shot object detection are introduced. According to the improvement strategies of different methods, few-shot object detection is divided into four types: attention mechanism, graph convolutional neural network, metric learning and data augmentation. The public datasets and evaluation metrics used in these methods are explained. Advantages, disadvantages, applicable scenarios of different methods, and performance on different datasets are compared and analyzed. Finally, the practical application fields and future research trends of few-shot object detection are discussed. Reference | Related Articles | Metrics

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Review of Medical Image Segmentation Based on UNet XU Guangxian, FENG Chun, MA Fei Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1776-1792.   DOI: 10.3778/j.issn.1673-9418.2301044 As one of the most important semantic segmentation frameworks in convolutional neural networks (CNN), UNet is widely used in image processing tasks such as classification, segmentation, and target detection of medical images. In this paper, the structural principles of UNet are described, and a comprehensive review of UNet-based networks and variant models is presented. The model algorithms are fully investigated from several perspectives, and an attempt is made to establish an evolutionary pattern among the models. Firstly, the UNet variant models are categorized according to the seven medical imaging systems they are applied to, and the algorithms with similar core composition are compared and described. Secondly, the principles, strengths and weaknesses, and applicable scenarios of each model are analyzed. Thirdly, the main UNet variant networks are summarized in terms of structural principles, core composition, datasets, and evaluation metrics. Finally, the inherent shortcomings and solutions of the UNet network structure are objectively described in light of the latest advances in deep learning, providing directions for continued improvement in the future. At the same time, other technological evolutions and application scenarios that can be combined with UNet are detailed, and the future development trend of UNet-based variant networks is further envisaged. Reference | Related Articles | Metrics

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Review of Graph Neural Networks Applied to Knowledge Graph Reasoning SUN Shuifa, LI Xiaolong, LI Weisheng, LEI Dajiang, LI Sihui, YANG Liu, WU Yirong Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 27-52.   DOI: 10.3778/j.issn.1673-9418.2207060 As an important element of knowledge graph construction, knowledge reasoning (KR) has always been a hot topic of research. With the deepening of knowledge graph application research and the expanding of its scope, graph neural network (GNN) based KR methods have received extensive attention due to their capability of obtaining semantic information such as entities and relationships in knowledge graph, high interpretability, and strong reasoning ability. In this paper, firstly, basic knowledge and research status of knowledge graph and KR are summarized. The advantages and disadvantages of KR approaches based on logic rules, representation learning, neural network and graph neural network are briefly introduced. Secondly, the latest progress in KR based on GNN is comprehensively summarized. GNN-based KR methods are categorized into knowledge reasoning based on recurrent graph neural networks (RecGNN), convolutional graph neural networks (ConvGNN), graph auto-encoders (GAE) and spatial-temporal graph neural networks (STGNN). Various typical network models are introduced and compared. Thirdly, this paper introduces the application of KR based on graph neural network in health care, intelligent manufacturing, military, transportation, etc. Finally, the future research directions of GNN-based KR are proposed, and related research in various directions in this rapidly growing field is discussed. Reference | Related Articles | Metrics

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Overview of Facial Deepfake Video Detection Methods ZHANG Lu, LU Tianliang, DU Yanhui Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 1-26.   DOI: 10.3778/j.issn.1673-9418.2205035 The illegal use of deepfake technology will have a serious impact on social stability, personal reputation and even national security. Therefore, it is imperative to develop research on facial deepfake videos detection tech-nology, which is also a research hotspot in the field of computer vision in recent years. At present, the research is based on traditional face recognition and image classification technology, building a deep neural network to deter-mine a facial video is real or not, but there are still problems such as the low quality of dataset, the combine of multimodal features and the poor performance of model generalization. In order to further promote the development of deepfake video detection technology, a comprehensive summary of various current algorithms is carried out, and the existing algorithms are classified, analyzed and compared. Firstly, this paper mainly introduces the facial deepfake videos detection datasets. Secondly, taking feature selection as the starting point, this paper summarizes the main method of detecting deepfake videos in the past three years, classifies various detection technologies from the pers-pectives of spatial features, spatial-temporal fusion features and biological features, and introduces some new detec-tion methods based on watermarking and blockchain. Then, this paper introduces the new trends of facial deepfake video detection methods from the aspects of feature selection, transfer learning, model architecture and training ideas. Finally, the full text is summarized and the future technology development is prospected. Reference | Related Articles | Metrics

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Survey on Pseudo-Labeling Methods in Deep Semi-supervised Learning LIU Yafen, ZHENG Yifeng, JIANG Lingyi, LI Guohe, ZHANG Wenjie Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1279-1290.   DOI: 10.3778/j.issn.1673-9418.2111144 With the development of intelligent technology, deep learning has become a hot topic in machine learning. It is playing a more and more important role in various fields. Deep learning requires a lot of labeled data to imp-rove model performance. Therefore, researchers effectively combine semi-supervised learning with deep learning to solve the labeled data problem. It utilizes a small amount of labeled data and a large amount of unlabeled data to build the model simultaneously. It can help to expand the sample space. In view of its theoretical significance and practical application value, this paper focuses on the pseudo-labeling methods as the starting point. Firstly, deep semi-supervised learning is introduced and the advantage of pseudo-labeling methods is pointed out. Secondly, the pseudo-labeling methods are described from self-training and multi-view training and the existing model is comprehensively analyzed. And then, the label propagation method based on graph and pseudo-labeling is introduced. Furthermore, the existing pseudo-labeling methods are analyzed and compared. Finally, the problems and future research direction of pseudo-labeling methods are summarized from the utility of unlabeled data, noise data, rationality, and the combi-nation of pseudo-labeling methods. Table and Figures | Reference | Related Articles | Metrics

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Survey on Cross-Chain Protocols of Blockchain MENG Bo, WANG Yibing, ZHAO Can, WANG Dejun, MA Binhao Journal of Frontiers of Computer Science and Technology    2022, 16 (10): 2177-2192.   DOI: 10.3778/j.issn.1673-9418.2203032 With the development of blockchain technology, due to the different system architecture and application scenarios of blockchain platforms, it is difficult to realize the interconnection and intercommunication of data and assets on different blockchains, which affects the promotion and application of blockchain. The cross-chain tech-nology of blockchain is an important technical solution to realize the interconnection of blockchain and improve the interoperability and extensibility of blockchain. The blockchain cross-chain protocol is the specific design specifi-cations to realize the cross-chain interoperability between different blockchains through cross-chain technology, so it is of great significance to the realization of blockchain interoperability and the construction of blockchain cross-chain application. This paper systematically arranges and analyzes the latest researches on the integration and implemen-tation of blockchain cross-chain protocols, and places them in four hierarchies: Firstly, the current research status of blockchain cross-chain interoperability is explained from three aspects, Internet of blockchains, cross-chain techno-logy and blockchain interoperability. Secondly, the cross-chain protocols are divided into cross-chain communi-cation agreements, cross-chain asset transaction agreements and cross-chain smart contract call agreements, and the latest research is analyzed. Thirdly, the key design principles of cross-chain protocols are summarized, and the solutions for the problems of security, privacy and scalability of cross-chain protocol are provided. Finally, com-bined with the actual needs of blockchain cross-chain applications, the future research direction of blockchain cross-chain protocol is given. Table and Figures | Reference | Related Articles | Metrics

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Survey of Camouflaged Object Detection Based on Deep Learning SHI Caijuan, REN Bijuan, WANG Ziwen, YAN Jinwei, SHI Ze Journal of Frontiers of Computer Science and Technology    2022, 16 (12): 2734-2751.   DOI: 10.3778/j.issn.1673-9418.2206078 Camouflaged object detection (COD) based on deep learning is an emerging visual detection task, which aims to detect the camouflaged objects “perfectly” embedded in the surrounding environment. However, most exiting work primarily focuses on building different COD models with little summary work for the existing methods. Therefore, this paper summarizes the existing COD methods based on deep learning and discusses the future development of COD. Firstly, 23 existing COD models based on deep learning are introduced and analyzed according to five detection mechanisms: coarse-to-fine strategy, multi-task learning strategy, confidence-aware learning strategy, multi-source information fusion strategy and transformer-based strategy. The advantages and disadvantages of each strategy are analyzed in depth. And then, 4 widely used datasets and 4 evaluation metrics for COD are introduced. In addition, the performance of the existing COD models based on deep learning is compared on four datasets, including quantitative comparison, visual comparison, efficiency analysis, and the detection effects on camouflaged objects of different types. Furthermore, the practical applications of COD in medicine, industry, agriculture, military, art, etc. are mentioned. Finally, the deficiencies and challenges of existing methods in complex scenes, multi-scale objects, real-time performance, practical application requirements, and COD in other multimodalities are pointed out, and the potential directions of COD are discussed. Table and Figures | Reference | Related Articles | Metrics

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Survey of Research on Instance Segmentation Methods HUANG TAO, LI Hua, ZHOU Gui, LI Shaobo, WANG Yang Journal of Frontiers of Computer Science and Technology    2023, 17 (4): 810-825.   DOI: 10.3778/j.issn.1673-9418.2209051 In recent years, with the continuous improvement of computing level, the research of instance segment-ation methods based on deep learning has made great breakthroughs. Image instance segmentation can distinguish different instances of the same class in images, which is an important research direction in the field of computer vision with broad research prospects, and has achieved great actual application value in scene comprehension, medical image analysis, machine vision, augmented reality, image compression, video monitoring, etc. Recently, instance segmentation methods have been updated more and more frequently, but there is a little literature to comprehensively and systematically analyze the research background related to instance segmentation. This paper  provides a comprehensive and systematic analysis and summary of the image instance segmentation methods based on deep learning. Firstly, this paper introduces the currently used common public datasets and evaluation indexes in instance segmentation, and analyzes the challenges of current datasets. Secondly, this paper respectively combs and summarizes the instance segmentation algorithms in the characteristics of two-stage segmentation methods and single-stage segmentation methods, elaborates their central ideas and design thoughts, and summarizes the advantages and shortcomings of the two types of methods. Thirdly, this paper evaluates the segmentation accuracy and speed of the models on a public dataset. Finally, this paper summarizes the current difficulties and challenges of instance segmentation, presents the solution ideas for facing the challenges, and makes a prospect for future research directions. Reference | Related Articles | Metrics

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Survey of Zero-Shot Image Classification LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai Journal of Frontiers of Computer Science and Technology    2021, 15 (5): 812-824.   DOI: 10.3778/j.issn.1673-9418.2010092 It is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly, the zero-shot learning, including direct push zero-shot learning and inductive zero-shot learning, is introduced briefly. Secondly, the space embedding zero-shot image classification methods and the generative model based zero-shot image classification methods with their subclass methods are introduced emphatically. Meanwhile, the mechanism, advantages and disadvantages, and application scenarios of these methods are analyzed and summarized. Thirdly, the main datasets and main evaluation criteria for zero-shot image classification are briefly introduced, and the performance of typical zero-shot image classification methods is compared. Then, the problems such as domain drift, hubness and semantic gap and the corresponding solutions are pointed out. Finally, the future development trends and research hotspots of zero-shot image classification are discussed, such as the accurate location of discriminative region, visual features of high-quality unseen class, generalized zero-shot image classification, etc. Reference | Related Articles | Metrics

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Survey of Personalized Learning Recommendation WU Zhengyang, TANG Yong, LIU Hai Journal of Frontiers of Computer Science and Technology    2022, 16 (1): 21-40.   DOI: 10.3778/j.issn.1673-9418.2105111 Personalized learning recommendation is a research field of intelligent learning. Its goal is to provide specific learners with effective learning resources on the learning platform, thereby enhancing learning enthusiasm and learning effect. Although the existing recommendation methods have been widely used in learning scenarios, the scientific rules of learning activities make personalized learning recommendations unique in terms of personalized parameter setting, recommendation goal setting, and evaluation standard design. In response to the above-mentioned problems, the research of personalized learning recommendation in recent years is reviewed on the basis of investigating a large number of literatures. The research on personalized learning recommendation is systematically sorted out and interpreted from five aspects, i.e., the general framework of learning recommendation, learner modeling, learning recommendation object modeling, learning recommendation algorithm, and learning recommendation evaluation. Firstly, the general framework of learning recommendation system is proposed. Secondly, the ideas and methods of learner modeling are introduced. Next, the ideas and methods of learning recommendation object modeling are discussed. Then, this paper summarizes the algorithm and model of learning recommendation. The following, this paper summarizes the design and method of learning recommendation evaluation. This paper also analyzes the main ideas, implementation plans, advantages and disadvantages of the existing research in these five aspects. Finally, this paper also looks forward to the future development direction of personalized learning recommendation, which lays foundation for further in-depth research on intelligent learning. Table and Figures | Reference | Related Articles | Metrics

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Deep Learning Method for Fine-Grained Image Categorization LI Xiangxia, JI Xiaohui, LI Bin Journal of Frontiers of Computer Science and Technology    2021, 15 (10): 1830-1842.   DOI: 10.3778/j.issn.1673-9418.2103019 Fine-grained image categorization aims to distinguish the sub-categories from a certain category of images. Generally, fine-grained data sets have the characteristics of the intra-class similarity and inter-class variation, which makes the task of fine-grained image categorization more challenging. With the increasing development of deep learning, the methods of fine-grained image categorization based on deep learning exhibit more powerful feature representation and generalization capabilities, and can obtain more accurate and stable classification results. Therefore, deep learning has been attracting more and more attentions and research from the researchers in the fine-grained image categorization. In this paper, starting from the background of fine-grained image categorization,  the difficulties and the meaning of fine-grained image categorization are introduced. Then, from the perspectives of strong supervision and weak supervision, this paper reviews the research progress of fine-grained image classification algorithms based on deep learning, and a variety of typical classification algorithms with excellent performance are introduced. In addition, the YOLO (you only look once), multi-scale CNN (convolutional neural network), and GAN (generative adversarial networks) model are further discussed in the application of fine-grained image categorization, the perfor-mance of the latest relevant fine-grained data augmentation methods is compared and an analysis of different types of fine-grained categorization methods is made under complex scenarios. Finally, by comparing and summarizing the categorization algorithms, the future improvement directions and challenges are discussed. Reference | Related Articles | Metrics

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Research Progress of Lightweight Neural Network Convolution Design MA Jinlin, ZHANG Yu, MA Ziping, MAO Kaiji Journal of Frontiers of Computer Science and Technology    2022, 16 (3): 512-528.   DOI: 10.3778/j.issn.1673-9418.2107056 Traditional neural networks have the disadvantages of over-reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power. The application development of artificial intelligence technology is limited to a certain extent. However, with the advent of the technological age, artificial intelligence, which is affected by user requirements, urgently needs to be able to successfully perform operations such as computer vision applications on portable devices. For this reason, this paper takes the convolution of popular lightweight neural networks in recent years as the research object. Firstly, by introducing the concept of lightweight neural network, the development status of lightweight neural networks and the problems faced by convolution in the network are introduced. Secondly, the convolution is divided into three aspects: lightweight of convolution structure, lightweight of convolution module and lightweight of convolution operation, specifically through the study of the convolution design in various lightweight neural network models, the lightweight effects of different convolutions are demonstrated, and the advantages and disadvantages of the optimization methods are explained. Finally, the main ideas and usage methods of all lightweight model convolutional design in this paper are summarized and analyzed, and their possible future development is prospected. Table and Figures | Reference | Related Articles | Metrics

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Survey of Data Pricing CAI Li, HUANG Zhenhong, LIANG Yu, ZHU Yangyong Journal of Frontiers of Computer Science and Technology    2021, 15 (9): 1595-1606.   DOI: 10.3778/j.issn.1673-9418.2103069 Data pricing is the behavior of taking data as assets and pricing the assets. In the current data markets, there is little transparency and information asymmetry between buyers and sellers, resulting in confusion in data pricing. If there is a standard process and evaluation method for data pricing, buyers can obtain datasets they need with a reasonable price, moreover, it would also improve the efficiency of data trading markets. This paper retrieves the relevant literatures about data pricing in recent years, and then summarizes the definition, characteristics, development and application scenarios of data pricing. It describes the data transaction process and data transaction costs, and focuses on describing two important research hotspots that affect data pricing, data pricing strategy and data pricing model. It comprehensively evaluates the mechanisms, advantages, disadvantages, and application scenarios of the existing six data pricing strategies and five pricing models. Finally, it analyzes the challenges of data pricing from three aspects: data value evaluation, data transaction rule and data privacy protection. And it prospects the future development trends of data pricing. The research results of this paper will provide valuable reference and foundation for future relevant work. Reference | Related Articles | Metrics

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Review on FPGA-Based Accelerators in Deep Learning LIU Tengda, ZHU Junwen, ZHANG Yiwen Journal of Frontiers of Computer Science and Technology    2021, 15 (11): 2093-2104.   DOI: 10.3778/j.issn.1673-9418.2104012 For the past few years, with rapid development of Internet and big data,  artificial intelligence has become popular, and it is the rise of deep learning that promotes the rapid development of AI. The problem that needs to be solved urgently in the era of big data is how to effectively analyze and use extremely complex and diverse data, and then make full use of the value of data and benefit mankind. As a technology of machine learning, deep learning which has been widely used in speech recognition, image recognition, natural language processing and many other fields is an important magic weapon to solve this problem. It plays an increasingly important role in data processing and changes traditional machine learning methods. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. With strong parallel computing power and low power consumption, FPGA has become a strong competitor of GPU in the field of deep learning acceleration. Starting from the typical models of deep learning, on the basis of the existing characteristics of FPGA acceleration technology, the research status of various accelerators is summarized from four aspects: accelerators for neural network models, accelerators for a specific application, accelerators for optimization strategies, and general accelerator frameworks with hardware templates. Then, the performance of different acceleration technologies in different models is compared. Finally, the possible development direction in the future is prospected. Reference | Related Articles | Metrics

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Review of Reinforcement Learning for Combinatorial Optimization Problem WANG Yang, CHEN Zhibin, WU Zhaorui, GAO Yuan Journal of Frontiers of Computer Science and Technology    2022, 16 (2): 261-279.   DOI: 10.3778/j.issn.1673-9418.2107040 The solution methods for combinatorial optimization problem (COP) have permeated to the fields of artificial intelligence, operations research, etc. With the scale of data increasing and the speed of problem updating being faster, the traditional method of solving the COP is challenged in computational speed, precision and generali-zation ability. In recent years, reinforcement learning (RL) has been widely used in driverless, industrial automation and other fields, showing strong decision-making and learning ability. Thus, many researchers have strived to use RL to solve COP, which provides a novel method for solving these problems. This paper firstly introduces the common COP problems and the basic principles of RL. Then, this paper elaborates the difficulties of RL in solving COP, analyzes the advantages of RL in combinatorial optimization (CO) field, and studies the principle of the combina-tion of RL and COP. Subsequently, this paper summarizes the theoretical methods and applied researches of solving COP problems utilizing RL in recent years. In order to highlight the superiority of RL model, this paper also com-pares and analyzes the key points, algorithmic logic and optimization effect of various representative researches in solving COP problem, and sums up the limitations of different methods and their application fields. Finally, this paper proposes four potential research directions. Table and Figures | Reference | Related Articles | Metrics

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