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2020 Conference Publication Adversarial bipartite graph learning for video domain adaptationLuo, Yadan, Huang, Zi, Wang, Zijian, Zhang, Zheng and Baktashmotlagh, Mahsa (2020). Adversarial bipartite graph learning for video domain adaptation. ACM International Conference on Multimedia, Seattle, WA, United States, 12-16 October 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3394171.3413897 |
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2020 Journal Article CRSAL: conversational recommender systems with adversarial learningRen, Xuhui, Yin, Hongzhi, Chen, Tong, Wang, Hao, Hung, Nguyen Quoc Viet, Huang, Zi and Zhang, Xiangliang (2020). CRSAL: conversational recommender systems with adversarial learning. ACM Transactions on Information Systems, 38 (4) 3394592, 1-40. doi: 10.1145/3394592 |
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2020 Journal Article Suicidal ideation detection: a review of machine learning methods and applicationsJi, Shaoxiong, Pan, Shirui, Li, Xue, Cambria, Erik, Long, Guodong and Huang, Zi (2020). Suicidal ideation detection: a review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8 (1) 9199553, 214-226. doi: 10.1109/tcss.2020.3021467 |
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2020 Conference Publication Completely unsupervised cross-modal hashingDuan, Jiasheng, Zhang, Pengfei and Huang, Zi (2020). Completely unsupervised cross-modal hashing. 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020, Jeju, South Korea, 24 - 27 September 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-59410-7_11 |
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2020 Conference Publication Try this instead: personalized and interpretable substitute recommendationChen, Tong, Yin, Hongzhi, Ye, Guanhua, Huang, Zi, Wang, Yang and Wang, Meng (2020). Try this instead: personalized and interpretable substitute recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401042 |
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2020 Conference Publication GAG: global attributed graph neural network for streaming session-based recommendationQiu, Ruihong, Yin, Hongzhi, Huang, Zi and Chen, Tong (2020). GAG: global attributed graph neural network for streaming session-based recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China , 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401109 |
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2020 Conference Publication Progressive graph learning for open-set domain adaptationLuo, Yadan, Wang, Zijian, Huang, Zi and Baktashmotlagh, Mahsa (2020). Progressive graph learning for open-set domain adaptation. International Conference on Machine Learning, Virtual, 13-18 July 2020. San Diego, CA USA: ML Research Press. |
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2020 Journal Article Correction to: Deep-sea organisms tracking using dehazing and deep learningLu, Huimin, Uemura, Tomoki, Wang, Dong, Zhu, Jihua, Huang, Zi and Kim, Hyoungseop (2020). Correction to: Deep-sea organisms tracking using dehazing and deep learning. Mobile Networks and Applications, 25 (6), 2536-2536. doi: 10.1007/s11036-020-01600-9 |
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2020 Journal Article Urban multimedia computing: Emerging methods in multimedia computing for urban data analysis and applicationsMin, Weiqing, Tian, Yonghong, Huang, Zi, Cheng, Wen-Huang and El Saddik, Abdulmotaleb (2020). Urban multimedia computing: Emerging methods in multimedia computing for urban data analysis and applications. IEEE Multimedia, 27 (3) 9186254, 8-11. doi: 10.1109/MMUL.2020.3017877 |
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2020 Conference Publication Human consensus-oriented image captioningWang, Ziwei, Huang, Zi and Luo, Yadan (2020). Human consensus-oriented image captioning. Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, 7-15 January 2021. Palo Alto, CA, United States: AAAI Press. doi: 10.24963/ijcai.2020/92 |
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2020 Journal Article Exploiting cross-session information for session-based recommendation with graph neural networksQiu, Ruihong, Huang, Zi, Li, Jingjing and Yin, Hongzhi (2020). Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Transactions on Information Systems, 38 (3) 22, 1-23. doi: 10.1145/3382764 |
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2020 Journal Article Deep collaborative discrete hashing with semantic-invariant structure constructionWang, Zijian, Zhang, Zheng, Luo, Yadan, Huang, Zi and Shen, Heng Tao (2020). Deep collaborative discrete hashing with semantic-invariant structure construction. IEEE Transactions on Multimedia, 23 9096547, 1274-1286. doi: 10.1109/tmm.2020.2995267 |
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2020 Conference Publication Semantics-reconstructing hashing for cross-modal retrievalZhang, Peng-Fei, Huang, Zi and Zhang, Zheng (2020). Semantics-reconstructing hashing for cross-modal retrieval. 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, Singapore, 11 - 14 May 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-47436-2_24 |
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2020 Conference Publication Learning from the past: continual meta-learning with Bayesian Graph Neural NetworksLuo, Yadan, Huang, Zi, Zhang, Zheng, Wang, Ziwei, Baktashmotlagh, Mahsa and Yang, Yang (2020). Learning from the past: continual meta-learning with Bayesian Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence/ The Thirty-Second Conference on Innovative Applications of Artificial Intelligence/ The Tenth Symposium on Educational Advances in Artificial Intelligence, New York, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v34i04.5942 |
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2020 Journal Article Flexible discrete multi-view hashing with collective latent feature learningLiu, Luyao, Zhang, Zheng and Huang, Zi (2020). Flexible discrete multi-view hashing with collective latent feature learning. Neural Processing Letters, 52 (3), 1765-1791. doi: 10.1007/s11063-020-10221-y |
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2020 Conference Publication CANZSL: Cycle-consistent adversarial networks for zero-shot learning from natural languageChen, Zhi, Li, Jingjing, Luo, Yadan, Huang, Zi and Yangyang, Yangyang (2020). CANZSL: Cycle-consistent adversarial networks for zero-shot learning from natural language. IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO United States, 1-5 March 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV45572.2020.9093610 |
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2020 Conference Publication Next point-of-interest recommendation on resource-constrained mobile devicesWang, Qinyong, Yin, Hongzhi, Chen, Tong, Huang, Zi, Wang, Hao, Zhao, Yanchang and Viet Hung, Nguyen Quoc (2020). Next point-of-interest recommendation on resource-constrained mobile devices. WWW '20: The Web Conference 2020, Taipei, Taiwan, April 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3366423.3380170 |
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2020 Conference Publication GCN-based user representation learning for unifying robust recommendation and fraudster detectionZhang, Shijie, Yin, Hongzhi, Chen, Tong, Hung, Quoc Viet Nguyen, Huang, Zi and Cui, Lizhen (2020). GCN-based user representation learning for unifying robust recommendation and fraudster detection. SIGIR '20: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, July 2020. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3397271.3401165 |
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2020 Conference Publication Graph-based relation-aware representation learning for clothing matchingLi, Yang, Luo, Yadan and Huang, Zi (2020). Graph-based relation-aware representation learning for clothing matching. Australasian Database Conference, Melbourne, VIC, Australia, 3-7 February 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-39469-1_15 |
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2020 Conference Publication Progressive graph learning for open-set domain adaptationLuo, Yadan, Wang, Zijian, Huang, Zi and Baktashmotlagh, Mahsa (2020). Progressive graph learning for open-set domain adaptation. 37th International Conference on Machine Learning ICML 2020, Vienna, Austria, 12-18 July 2020 . International Machine Learning Society . |
