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  • Open Access

    ARTICLE

    Criss-Cross Attentional Siamese Networks for Object Tracking

    Zhangdong Wang1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2931-2946, 2022, DOI:10.32604/cmc.2022.028896 - 16 June 2022

    Abstract Visual object tracking is a hot topic in recent years. In the meanwhile, Siamese networks have attracted extensive attention in this field because of its balanced precision and speed. However, most of the Siamese network methods can only distinguish foreground from the non-semantic background. The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC) can achieve higher precision under interferences, but the tracking accuracy is still not ideal, especially in the environment with more target interferences, dim light, and shadows. In this paper, we propose criss-cross attentional Siamese networks for object tracking (SiamCC). To More >

  • Open Access

    ARTICLE

    Anchor-free Siamese Network Based on Visual Tracking

    Shaozhe Guo1, Yong Li1,*, Xuyang Chen2, Youshan Zhang1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3137-3148, 2022, DOI:10.32604/cmc.2022.026784 - 16 June 2022

    Abstract The Visual tracking problem can usually be solved in two parts. The first part is to extract the feature of the target and get the candidate region. The second part is to realize the classification of the target and the regression of the bounding box. In recent years, Siameses network in visual tracking problem has always been a frontier research hotspot. In this work, it applies two branches namely search area and tracking template area for similar learning to track. Some related researches prove the feasibility of this network structure. According to the characteristics of… More >

  • Open Access

    ARTICLE

    Reference Selection for Offline Hybrid Siamese Signature Verification Systems

    Tsung-Yu Lu1, Mu-En Wu2, Er-Hao Chen3, Yeong-Luh Ueng4,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 935-952, 2022, DOI:10.32604/cmc.2022.026717 - 18 May 2022

    Abstract This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a More >

  • Open Access

    ARTICLE

    FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

    Yingchao Yu*, Shuitao Gan, Xiaojun Qin

    Journal on Internet of Things, Vol.4, No.1, pp. 1-20, 2022, DOI:10.32604/jiot.2022.019469 - 16 May 2022

    Abstract In recent years, with the development of the natural language processing (NLP) technologies, security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity, achieved great progress. However, we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of instructions. In this paper, we propose firmVulSeeker—a vulnerability search tool for embedded firmware images, based on BERT and Siamese network. It first builds a BERT MLM task to observe and learn the semantics of… More >

  • Open Access

    ARTICLE

    Vehicle Matching Based on Similarity Metric Learning

    Yujiang Li1,2, Chun Ding1,2, Zhili Zhou1,2,*

    Journal of New Media, Vol.4, No.1, pp. 51-58, 2022, DOI:10.32604/jnm.2022.028775 - 21 April 2022

    Abstract With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity More >

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