Publications [Google Scholar] [DBLP]

CONFERENCE PAPERS

  1. Jiamian Wang, Pichao Wang, Dongfang Liu, Qiang Guan, Sohail Dianat, Majid Rabbani, Raghuveer Rao, and Zhiqiang Tao, "Diffusion-Inspired Truncated Sampler for Text-Video Retrieval", NeurIPS, 2024.
  2. Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, and Zhiqiang Tao, "Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging", NeurIPS, 2024.
  3. Guohao Sun, Can Qin, Huazhu Fu, Linwei Wang, and Zhiqiang Tao, "Self-Training Large Language and Vision Assistant for Medical", EMNLP, 2024.
  4. Guohao Sun, Can Qin, Jiamian Wang, Zeyuan Chen, Ran Xu, and Zhiqiang Tao, "SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant", ECCV, 2024.
  5. Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao, and Yi Fang, "Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers", NAACL, 2024.
  6. Jiamian Wang, Guohao Sun, Pichao Wang, Dongfang Liu, Sohail Dianat, Majid Rabbani, Raghuveer Rao, and Zhiqiang Tao, "Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval", CVPR, 2024
  7. Guohao Sun, Yue Bai, Xueying Yang, Yi Fang, Yun Fu, and Zhiqiang Tao, "Aligning Out-of-Distribution Web Images and Caption Semantics via Evidential Learning", ACM Web Conference (WWW), 2024.
  8. Dingrong Wang, Hitesh Sapkota, Zhiqiang Tao, and Qi Yu, "Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness", KDD, 2024.
  9. Cheng Han, Yawen Lu, Guohao Sun, James Chenhao Liang, Zhiwen Cao, Qifan Wang, Qiang Guan, Sohail A. Dianat, Raghuveer M. Rao, Tong Geng, Zhiqiang Tao, and Dongfang Liu, "Prototypical Transformer as Unified Motion Learners", ICML, 2024.
  10. Wei Feng, Guoshuai Sheng, Qianqian Wang, Quanxue Gao, Zhiqiang Tao, and Bo Dong, "Partial Multi-view Clustering via Self-Supervised Network", AAAI, 2024.
  11. Wei Feng, Zhenwei Wu, Qianqian Wang, Bo Dong, Zhiqiang Tao, and Quanxue Gao, "Federated Multi-View Clustering via Tensor Factorization", IJCAI, 2024.
  12. Wei Feng, Zhenwei Wu, Qianqian Wang, Bo Dong, Zhiqiang Tao, and Quanxue Gao, "Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means", IJCAI, 2024.
  13. Bowen Zhao, Qianqian Wang, Zhiqiang Tao, Wei Feng, and Quanxue Gao, "DFMVC: Deep Fair Multi-view Clustering", ACM MM, 2024.
  14. Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu, and Zhiqiang Tao, "Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution", ICCV, 2023.
  15. Hitesh Sapkota, Dingrong Wang, Zhiqiang Tao, and Qi Yu, "Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training", NeurIPS, 2023.
  16. Zihao Zhang, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, Wei Feng, "Dropping Pathways Towards Deep Multi-View Graph Subspace Clustering Networks", ACM MM 2023.
  17. Weili Shi, Xueying Yang, Xujiang Zhao, Haifeng Chen, Zhiqiang Tao, and Sheng Li. Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning, ACM CIKM, 2023.
  18. Yuan Wang, Peifeng Yin, Zhiqiang Tao, Hari Venkatesan, Jin Lai, Yi Fang, and PJ Xiao, "An Empirical Study of Selection Bias in Pinterest Ads Retrieval", KDD, 2023.
  19. Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, and Yun Fu, "Parameter-efficient masking networks", NeurIPS, 2022. [Code]
  20. Xueying Yang, Jiamian Wang, Xujiang Zhao, Sheng Li, and Zhiqiang Tao, "Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty", CIKM, 2022. [Code]
  21. Jiamian Wang, Yulun Zhang, Xin Yuan, Ziyi Meng, and Zhiqiang Tao, "Modeling Mask Uncertainty in Hyperspectral Image Reconstruction", ECCV, 2022. [Code] (Oral presentation, Accept rate: 158/5803=2.7%)
  22. Yuan Wang, Zhiqiang Tao, and Yi Fang, "A Meta-learning Approach to Fair Ranking", ACM SIGIR, 2022.
  23. Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, and Yun Fu, "Dual Lottery Ticket Hypothesis", ICLR, 2022. [Code]
  24. Yue Bai, Zhiqiang Tao, Lichen Wang, Sheng Li, Yu Yin, and Yun Fu, "Collaborative Attention Mechanism for Multi-Modal Time Series Classification", SDM, 2022.
  25. Qianqian Wang, Wei Xia, Zhiqiang Tao, Quanxue Gao, and Xiaochun Cao, "Deep Self-Supervised t-SNE for Multi-modal Subspace Clustering", ACM MM, 2021.
  26. Ronghang Zhu, Zhiqiang Tao, Yaliang Li, and Sheng Li, "Automated Graph Learning via Population Based Self-Tuning GCN", ACM SIGIR, 2021. [Code]
  27. Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li, and Yun Fu, "Correlative Channel-Aware Fusion for Multi-View Time Series Classification ", AAAI, 2021. [Code]
  28. Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, and Yun Fu, "Learning to Mutate with Hypergradient Guided Population", NeurIPS, 2020.
  29. Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, and Quanxue Gao, "Multi-View Attribute Graph Convolution Networks for Clustering", IJCAI, 2020. [Code]
  30. Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, and Zhenhui Li, “Automated Relational Meta-learning”, ICLR, 2020. [Code]
  31. Lichen Wang, Zhengming Ding, Zhiqiang Tao, Yunyu Liu, and Yun Fu, “Generative Multi-View Human Action Recognition”, ICCV, 2019. [Code] (Oral presentation, Accept rate: 200/4300=4.6%)
  32. Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao, and Yun Fu, “Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit”, SIGKDD, 2019. (Oral presentation, Accept rate: 110/1200=9.2%)
  33. Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu, “Adversarial Graph Embedding for Ensemble Clustering”, IJCAI, 2019.
  34. Chaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, and Zhaohua Yang, “Deep Adversarial Network for Multi-view Clustering”, IJCAI, 2019.
  35. Songyao Jiang, Zhiqiang Tao, and Yun Fu, “Segmentation Guided Image-to-Image Translation with Adversarial Networks”, FG, 2019. [Code]
  36. Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu, “Partial Multi-View Clustering via Consistent GAN”, ICDM, 2018. [Code]
  37. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu, “From Ensemble Clustering to Multi-View Clustering”, IJCAI, 2017. [Code]
  38. Jun Li, Handong Zhao, Zhiqiang Tao, and Yun Fu, “Large-scale Subspace Clustering by Fast Regression Coding”, IJCAI, 2017.
  39. Yu Kong, Zhiqiang Tao, and Yun Fu, “Deep Sequential Context Networks for Action Prediction”, CVPR, 2017.
  40. Zhiqiang Tao, Hongfu Liu, Huazhu Fu, and Yun Fu, “Image Cosegmentation via Saliency-Guided Constraint Clustering with Cosine Similarity”, AAAI, 2017.
  41. Zhiqiang Tao, Hongfu Liu, and Yun Fu, “Simultaneous Clustering and Ensemble”, AAAI, 2017.
  42. Sheng Li, Hongfu Liu, Zhiqiang Tao, and Yun Fu, “Multi-View Graph Learning with Adaptive Label Propagation”, IEEE International Conference on Big Data, 2017.
  43. Zhiqiang Tao, Hongfu Liu, Sheng Li, and Yun Fu, “Robust Spectral Ensemble Clustering”, CIKM, 2016. [Code] (Oral presentation, Accept rate: 23%)
  44. Xiaochun Cao, Yupeng Cheng, Zhiqiang Tao, and Huazhu Fu, “Co-saliency Detection via Base Reconstruction”, ACM MM, 2014.
  45. Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Xuewei Li, “Saliency Map Fusion Based on Rank-one Constraint”, ICME, 2013.

JOURNAL PAPERS

  1. Yuan Wang, Zhiqiang Tao, and Yi Fang, "A Unified Meta-learning Framework for Fair Ranking with Curriculum Learning", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024. (IF: 8.9)
  2. Qianqian Wang, Zhiqiang Tao, Quanxue Gao, and Licheng Jiao, “Multi-view Subspace Clustering via Structured Multi-pathway Network”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (IF: 10.451)
  3. Qianqian Wang, Zhiqiang Tao, Wei Xia, Quanxue Gao, Xiaochun Cao, and Licheng Jiao, “Adversarial Multi-view Clustering Networks with Adaptive Fusion”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (IF: 10.451)
  4. Zhiqiang Tao, Jun Li, Huazhu Fu, Yu Kong, and Yun Fu, “From Ensemble Clustering to Subspace Clustering: Cluster Structure Encoding”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (IF: 10.451)
  5. Jun Li, Zhiqiang Tao, Bineng Zhong, Yue Wu, and Yun Fu, “Large-Scale Subspace Clustering by Independent Distributed and Parallel Coding”, IEEE Transactions on Cybernetics (TCYB), 2021. (IF: 11.448)
  6. Songyao Jiang, Zhiqiang Tao, and Yun Fu, “Geometrically Editable Image-to-Image Translation with Adversarial Networks”, IEEE Transactions on Image Processing (TIP), vol. 30, pp. 2771-2783, 2021. [Code] (IF: 10.856)
  7. Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu, “Generative Partial Multi-View Clustering with Adaptive Fusion and Cycle Consistency”, IEEE Transactions on Image Processing (TIP), vol. 30, pp. 1771-1783, 2021. [Code] (IF: 10.856)
  8. Jun Li, Hongfu Liu, Zhiqiang Tao, Handong Zhao, and Yun Fu, “Learnable Subspace Clustering”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020. [Code] (IF: 10.451)
  9. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu, “Marginalized Multi-View Ensemble Clustering”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 31, no. 2, pp. 600-611, 2020. [Code] (IF: 10.451)
  10. Yu Kong, Zhiqiang Tao, and Yun Fu, “Adversarial Action Prediction Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 42, no. 3, pp. 539-553, 2020. (IF: 16.389)
  11. Zhiqiang Tao, Hongfu Liu, Huazhu Fu, Yun Fu, "Multi-View Saliency-Guided Clustering for Image Cosegmentation", IEEE Transactions on Image Processing (TIP), vol. 28, no. 9, pp. 4634-4645, 2019. [iCoseg][InterNet][MSRC][Pascal10] (IF: 10.856)
  12. Sheng Li*, Zhiqiang Tao*, Kang Li, and Yun Fu, “Visual to Text: Survey of Image and Video Captioning”, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), vol. 3, no. 4, pp. 297-312, 2019. (* indicates equal contribution.)
  13. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu, “Robust Spectral Ensemble Clustering via Rank Minimization”, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 13, no. 1, pp. 4:1-4:25, 2019. (IF: 2.731)
  14. Hongfu Liu, Zhiqiang Tao, and Yun Fu, “Partition Level Constrained Clustering”, IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), vol. 40 , no. 10, pp. 2469-2483, 2018. [Code] (IF: 16.389)
  15. Changqing Zhang, Zhiqiang Tao, Xingxing Wei, and Xiaochun Cao, “A Flexible Framework of Adaptive Method Selection for Image Saliency Detection”, Pattern Recognition Letters (PRL), vol. 63, pp. 66-70, 2015. (IF: 2.81)
  16. Xingxing Wei, Zhiqiang Tao, Changqing Zhang, and Xiaochun Cao, “Structured Saliency Fusion Based on Dempster-Shafer Theory”, IEEE Signal Processing Letters (SPL), vol. 22, no. 9, pp. 1345-1349, 2014. (IF: 3.109)
  17. Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Wei Feng, “Self-adaptively Weighted Co-saliency Detection via Rank Constraint”, IEEE Transaction on Image Processing (TIP), vol. 23, no. 9, pp. 4175-4186, 2014. [Code] (IF: 10.856)