Home / Journals / JAI / Vol.2, No.2, 2020
  • Open Access

    REVIEW

    A Review of Object Detectors in Deep Learning

    Chen Song1, Xu Cheng1, *, Yongxiang Gu1, Beijing Chen1, Zhangjie Fu1
    Journal on Artificial Intelligence, Vol.2, No.2, pp. 59-77, 2020, DOI:10.32604/jai.2020.010193
    Abstract Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based on deep learning and state-of-the-art… More >

  • Open Access

    ARTICLE

    Improve Neural Machine Translation by Building Word Vector with Part of Speech

    Jinyingming Zhang1 , Jin Liu1, *, Xinyue Lin1
    Journal on Artificial Intelligence, Vol.2, No.2, pp. 79-88, 2020, DOI:10.32604/jai.2020.010476
    Abstract Neural Machine Translation (NMT) based system is an important technology for translation applications. However, there is plenty of rooms for the improvement of NMT. In the process of NMT, traditional word vector cannot distinguish the same words under different parts of speech (POS). Aiming to alleviate this problem, this paper proposed a new word vector training method based on POS feature. It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors. In the experiments, we conducted extensive experiments to evaluate our methods. The experimental result shows that the proposed method is… More >

  • Open Access

    ARTICLE

    Survey of Knowledge Graph Approaches and Applications

    Hangjun Zhou1, Tingting Shen1, *, Xinglian Liu1, Yurong Zhang1, Peng Guo1, 2, Jianjun Zhang3
    Journal on Artificial Intelligence, Vol.2, No.2, pp. 89-101, 2020, DOI:10.32604/jai.2020.09968
    Abstract With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been applied to various systems, the… More >

  • Open Access

    ARTICLE

    An Attention-Based Recognizer for Scene Text

    Yugang Li1, *, Haibo Sun1
    Journal on Artificial Intelligence, Vol.2, No.2, pp. 103-112, 2020, DOI:10.32604/jai.2020.010203
    Abstract Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. Although STR method has been greatly developed, the existing methods still can't recognize any shape of text, such as very rich curve text or rotating text in daily life, irregular scene text has complex layout in two-dimensional space, which is used to recognize scene text in the past Recently, some recognizers correct irregular text to regular text image with approximate 1D layout, or convert 2D image feature mapping to one-dimensional feature sequence. Although these methods have achieved good performance, their robustness and accuracy are limited due… More >

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