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Published in ICETCI 2023, 2023
This paper studies the performance ability of different deep learning models on WHDLD dataset.
Recommended citation: H. Zhu, X. Wang and R. Chen, "Deep Learning Algorithm Based Remote Sensing Image Classification Research," 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, 2023, pp. 1368-1373, doi: 10.1109/ICETCI57876.2023.10176407. https://ieeexplore.ieee.org/abstract/document/10176407
Published in KDD 2025, 2025
This paper proposes a dynamic equilibrium gradient-assigned attack against graph prompt learning, which can successfully attack the graph prompt learning model with a small amount of perturbations.
Recommended citation: Ju Jia, Jingxuan Yu, Di Wu, Cong Wu, Hengjie Zhu, and Lina Wang. 2025. Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 25). Association for Computing Machinery, New York, NY, USA, 1049–1060. https://doi.org/10.1145/3711896.3737091 https://dl.acm.org/doi/abs/10.1145/3711896.3737091
Published in AAAI 2026, 2026
This paper rethinks strict discretization for asymmetric image retrieval and proposes Deep Correlation Alignment Hashing (DCAH) with correlation alignment based quantization.
Recommended citation: Xinze Liu, Dayan Wu, Hengjie Zhu, Chenming Wu, and Pengwen Dai. 2026. Discretization Is Not Always Better: Rethinking Deep Quantization for Asymmetric Image Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 40(9), 7323-7331. https://doi.org/10.1609/aaai.v40i9.37670 https://doi.org/10.1609/aaai.v40i9.37670
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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