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• Haikun Jia, Huini Sun, Shuang Bai*. Decomposition LSTM with dual multi-head self-attention for wind turbine drivetrain state forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, vol.33, no.2, pp.127-144, 2025.
• Huini Sun, Hao Zheng, Shuang Bai*. Gaussian-Weighted Trend-Seasonal Decomposition Interactive Attention LSTM for Wind Turbine Drivetrain State Forecasting. International Conference on Vision, Image and Signal Processing, 2024.
• Yufeng Cheng, Dongxue Wang, Shuang Bai*, Jingkai Ma, Chen Liang，Kailong Liu, Tao Deng. Understanding document images by introducing explicit semantic information and short-range information interaction. Image and Vision Computing, doi.org/10.1016/j.imavis.2024.105392, 2024.
• JingKai Ma and Shuang Bai*, Multi-view Part-Based Few-shot Object Detection. IEEE Transactions on Neural Networks and Learning Systems, vol.36, no.8, pp.14749-14763, 2025.
• JingKai Ma and Shuang Bai*, SGFNet: Structure-Guided Few-Shot Object Detection. IEEE Transactions on Circuits and Systems for Video Technology, vol.35, no.4, pp.3209-3221, 2025.
• JingKai Ma and Shuang Bai*, Wenchao Pan. Boosting Few-Shot Semantic Segmentation with Prior-Driven Edge Feature Enhancement Network. IEEE Transactions on Artificial Intelligence, 10.1109/TAI.2024.3474650, 2024.
• Chen Liang and Shuang Bai*. Found Missing Semantics: Supplemental Prototype Network for Few-Shot Semantic Segmentation. Computer Vision and Image Understanding, 2024.
• Shuang Bai, Chen Liang, Zhen Wang, Wenchao Pan. Information entropy induced graph convolutional network for semantic segmentation. Journal of Visual Communication and Image Representation,vol.103,104217, 2024.
• Lingxing Kong, Kailong Liu, Deyi Fu, Boyong Liu,Jingkai Ma, Huini Sun, Shuang Bai*. Wind power regression prediction based on stacked LSTMs with attention mechanisms for evaluating technological improvement effects of wind turbines. Journal of Intelligent & Fuzzy Systems, pp.1-12, 2023.
• 白双，梁晨. 深度学习研究生课程教学探索与实践, 工业和信息化教育, 2023.
• Wenchao Gu and Shuang Bai*, Contour and Enclosed Region Refining for Contour-based Instance Segmentation, IEEE Transactions on Cognitive and Developmental Systems, vol.105, no.4, pp.2241-2253, 2023.
• Wenchao Gu, Shuang Bai*, Lingxing Kong, A review on 2D instance segmentation based on deep neural networks, Image and Vision Computing,Volume 120,2022.(SCI检索)
• Shuang Bai, Wenchao Gu, Lingxing Kong. Interweave features of Deep Convolutional Neural Networks for semantic segmentation. Engineering Applications of Artificial Intelligence, Volume 109, 2022.
• Shuang Bai, Congcong Wang, Information Aggregation and Fusion in Deep Neural Networks for Object Interaction Exploration for Semantic Segmentation, Knowledge-Based Systems, vol.218, 2021.
• Shuang Bai, Huadong Tang, Shan An, Coordinate CNNs and LSTMs to categorize scene images with multi-views and multi-levels of abstraction, Expert Systems With Applications, vol.120, 298–309, 2019.
• Shuang Bai, Chao Han, Shan An, Recognizing Anomalies in Urban Road Scenes Through Analysing Single Images Captured by Cameras on Vehicles, Sensing and Imaging, doi.org/10.1007/s11220-018-0218-7, 2018.
• Shuang Bai, Shan An, A survey on automatic image caption generation, Neurocomputing, vol.311, 291–304, 2018.
• Shuang Bai, Huadong Tang, Softly combining an ensemble of classifiers learned from a single convolutional neural network for scene categorization, Applied Soft Computing, vol.67, pp.183-196, 2018.
• Shuang Bai, Huadong Tang, Categorizing Scenes by Exploring Scene Part Information without Constructing Explicit Models, Neurocomputing, vol.281, pp.160-168, 2018.
• Shuang Bai, Zhenyao Liu, Chang Yao, Classify vehicles in traffic scene images with deformable part-based models, Machine Vision and Applications, vol.29, no.03, pp. 393-403, 2018.
• Shuang Bai, Scene Categorization Through Using Objects Represented by Deep Features, International Journal of Pattern Recognition and Artificial Intelligence, vol. 31, no.09, 2017.
• Shuang Bai, Growing Random Forest on Deep Convolutional Neural Networks for Scene Categorization, Expert Systems with Applications, vol. 71, pp. 279-287, 2017.
• Shuang Bai, Zhaohong Li, Jianjun Hou, Learning two-pathway convolutional neural networks for categorizing scene images, Multimedia Tools and Applications, vol.76, no.15, pp.16145-16162, 2017.
• Shuang Bai, Chao Han and Jianjun Hou, Enhancing details in images for performing scene categorization, Journal of Electronic Imaging, vol. 24, no.04, 2016.
• Shuang Bai, Jianjun Hou and Noboru Ohnishi, Scene categorization through combining LBP and SIFT features effectively, International Journal of Pattern Recognition and Artificial Intelligence, vol.30, no.01, 2016.
• Shuang Bai, Human-centric image categorization based on poselets, Sensing and Imaging, vol.16, no.01, pp.1-19, 2015.
• Shuang Bai, Jianjun Hou and Noboru Ohnishi, Combining LBP and SIFT in sparse coding for categorizing scene images, IEICE Transactions on Information and Systems, vol. E97-D, no.09, pp.2563-2566, 2014.
• Shuang Bai, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo and Noboru Ohnishi, A novel method for exploring patch-level context to improve image categorization performance, IEEJ Transactions on Electronics, Information and Systems, vol.133, no.12, pp.2264-2274, 2013.
• Shuang Bai, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo and Noboru Ohnishi, Informative Patches Sampling for Image Classification by Utilizing Bottom-up and Top-down Information, Machine Vision and Applications, vol. 24, no.05, pp 959-970, 2013.
• Shuang Bai, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo and Noboru Ohnishi, Incorporating Contextual Information into Bag of Visual Words Framework for Effective Object Categorization, IEICE Transactions on Information and Systems, vol.E95-D, no.12, pp.3060-3068, 2012.
• Shuang Bai, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo, Noboru Ohnishi, Incorporating Top-Down Guidance for Extracting Informative Patches for Image Classification, IEICE Transactions on Information and Systems, vol. E95-D, no. 03, pp.880-883, 2012.
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