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Xue Bai

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Proceedings Article | 22 August 2024 Paper

Research on BERT-based deep multilayer fusion Chinese short text classification method

Hao Wang, Xianghe Meng, Yugang Dai, Yuezhou Zhang, Jiaxin Wang, Xue Bai, Mengdi Ma, Xiangzhen He

Proceedings Volume 13228, 1322804 (2024) https://doi.org/10.1117/12.3038156

KEYWORDS: Performance modeling, Data modeling, Classification systems, Feature extraction, Machine learning, Deep learning, Education and training, Semantics, Neural networks, Mathematical optimization

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The categorization of brief Chinese texts, a critical area for extracting insights from data with limited information content, presents unique challenges such as limited word count, ambiguity, and non-standardized information. These factors complicate the extraction and representation of textual features. This study introduces the BERT-based BRLC (BERT Recurrent Layer Composition) model, customized for brief text categorization assignments. The model employs BERT as its foundation, utilizing the Transformer architecture to encode input text and produce a semantically rich vector representation. The BRLC component then captures both global and local sequential features of the text, culminating in classification outcomes through a non-linear transformation process. Evaluated against contemporary deep learning models, the BRLC model demonstrates superior classification performance and efficiency. Utilizing the THUNews dataset, our model attains a precision level of 94.92%, outperforming the original BERT by 2.19%. Comparative experiments confirm the BRLC model's effectiveness in enhancing the accuracy of short text classification tasks.

Proceedings Article | 13 June 2024 Paper

Exploration and research on the digital protection methods of ethnic dance

Yuezhou Zhang, Xiangzhen He, Jiaxin Wang, Xue Bai, Mengdi Ma

Proceedings Volume 13180, 131806X (2024) https://doi.org/10.1117/12.3033546

KEYWORDS: 3D modeling, Pose estimation

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Folk dance is an artistic treasure of Chinese traditional culture with a long history, and it is of great significance to explore how to digitize folk dance for preservation, display and research. This paper proposes to combine folk dance with 3D human posture estimation technology to explore the feasibility of storing folk dance as 3D skeletal movement files BVH (Biovision Hierarchy, BVH), in order to advance the development of the digitization process of folk dance. The datasets of three dance types, Daizu dance, Zangzu dance and Dunhuang dance, which are publicly available and field-collected, are utilized in the ethnic dance aspect; the main objective of the 3D human pose estimation aspect is to predict the pose and shape of the 3D human body from an RGB image or a sequence of multiple RGB images, and at the same time, to learn the attentional weights of each body part, so as to solve the occlusion problem that exists in the complex environments or complex movements, and to improve the method's Robustness. The method contains two main branches, the 2D part branch and the 3D body branch, where the 2D part branch is responsible for generating the attention weights while the 3D body branch is used to predict the pose and shape parameters. The model introduces a part-based attention mechanism in training which uses partially segmented labels to guide the attention branch in the early stages of training and unsupervised in the later stages in such a way that the attention mechanism is able to obtain effective information from the image and surrounding pixels unsupervised, and to focus more flexibly on the useful regions in the pose estimation. The whole model is supervised by a series of regression losses, and achieves better results in quantitative, qualitative and manual evaluations, exploring a new way for the digital preservation and presentation of folk dances.

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