AN ADVERSARIAL AND DEEP HASHING-BASED HIERARCHICAL SUPERVISED CROSS-MODAL IMAGE AND TEXT RETRIEVAL ALGORITHM, 77-86.

Ruidong Chen, Baohua Qiang, Mingliang Zhou, Shihao Zhang, Hong Zheng, and Chenghua Tang

References

  1. [1] C. Shihuan, L. Wanlin, W. Shangsheng, H. Mouxiao,C. Ruihong, and G. Weipeng, Indoor localization systemof ROS mobile robot based on visible light communication,International Journal of Robotics & Automation, 38(1), 2023,1–12.
  2. [2] Z. Weiyang, Y. Jianjun, C. Houru, G. Lianxin, and Z. Wei,Human back acupuncture points location using RGB-D imagefor TCM massage robots, International Journal of Robotics &Automation, 38(1), 2023, 67–75.
  3. [3] S. Chun, S. J. Oh, R. S. de Rezende, et al., Probabilisticembeddings for cross-modal retrieval, Proc. IEEE Conf. onComputer Vision and Pattern Recognition, Virtual Event, 2021,8415-8424.
  4. [4] W. Xie, M. Cui, M. Liu, P. Wang, and B. Qiang, Deephashing multi-label image retrieval with attention mechanism,International Journal of Robotics & Automation, 37(4), 2022,372-381.
  5. [5] Y. Feng, T. Tang, S. Chen, and Y. Wu, Automated defectdetection based on transfer learning and deep convolutiongenerative adversarial networks, International Journal ofRobotics & Automation, 36(6), 2021, 471–478.
  6. [6] H. Liu, S. Ren, D. Ren, and X. Liu, Automatic extraction oforchards from remote sensing image based on category attentionmechanism, International Journal of Robotics & Automation,37(1), 2022, 20-28.
  7. [7] D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, Canonicalcorrelation analysis: An overview with application to learningmethods, Neural Computation, 16(12), 2004, 2639–2664.
  8. [8] Q. Y. Jiang, and W. J. Li, Deep cross-modal hashing, Proc.30th IEEE Conf. on Computer Vision and Pattern Recognition,Honolulu, HI, 2017, 3270-3278.
  9. [9] C. Li, C. Deng, and N. Li, Self-supervised adversarial hashingnetworks for cross-modal retrieval, Proc. IEEE Conf. onComputer Vision and Pattern Recognition, Salt Lake City, UT,2018, 4242–4251.
  10. [10] D. Wang, H. Huang, C. Lu, B.S. Feng, G. Wen, L. Nie, andX.L. Mao, Supervised deep hashing for hierarchical labeleddata, Proc. 32th AAAI Conf. on Artificial Intelligence, NewOrleans, LA, 2018, 7388–7395.
  11. [11] C. Sun, X. Song, F. Feng, W.X. Zhao, H. Zhang, and L.Nie, Supervised hierarchical cross-modal hashing, Proc. 42ndInternational ACM Sigir Conf. on Research and Developmentin Information Retrieval, Paris, France, 2019, 725-734.
  12. [12] X. Zhai, Y. Peng, and J. Xiao, Learning cross-media jointrepresentation with sparse and semisupervised regularization,IEEE Transactions on Circuits and Systems for VideoTechnology, 24(6), 2014, 965–978.
  13. [13] Y. Gong, Q. Ke, M. Isard, and S. Lazebnik, A multi-viewembedding space for modeling internet images, tags, and theirsemantics, International Journal of Computer Vision, 106(2),2014, 210-233.
  14. [14] Y. Peng, X. Zhai, Y. Zhao, and X. Huang, Semi-supervisedcross-media feature learning with unified patch graphregularization, IEEE Transactions on Circuits and Systems forVideo Technology, 26(3), 2016, 583-596.84
  15. [15] Q. Chen, Z. Liu, Y. Zhang, K. Fu, Q. Zhao, and H. Du, RGB-Dsalient object detection via 3D convolutional neural networks,Proc. AAAI Conf. on Artificial Intelligence, Virtual Event,2021, 1063–1071.
  16. [16] R. Sun, L. Xuan, L. Hongyan, and W. Lu, Cultivated landsegmentation of remote sensing image based on PSPNeT ofattention mechanism, International Journal of Robotics &Automation, 37(1), 2022, 11–19.
  17. [17] G. Bao, and Y. Zhang, Contextualized rewriting for textsummarization, Proc. AAAI Conf. on Artificial Intelligence,Virtual Event, 2021, 12544–12553.
  18. [18] D. Wu, P. Liu, and Y. Zou, A novel method for extracting textfrom a geometric region, International Journal of Robotics &Automation, 36(5), 2021, 325–336.
  19. [19] Y. Peng, J. Qi, X. Huang, and Y. Yuan, CCL: Cross-modalcorrelation learning with multigrained fusion by hierarchicalnetwork, IEEE Transactions on Multimedia, 20(2), 2018,405–420.
  20. [20] F. Feng, X. Wang, and R. Li, Cross-modal retrieval withcorrespondence autoencoder, Proc. ACM Conf. on Multimedia,Univ Cent Florida, Orlando, FL, 2014, 7–16.
  21. [21] Y. Peng, X. Huang, and J. Qi, Cross-media sharedrepresentation by hierarchical learning with multipledeep networks, Proc. 25th International Joint Conf.on Artificial Intelligence, New York, NY, USA, 2016,3846–3853.
  22. [22] J. Yu, H. Zhou, Y. Zhan, and D. Tao, Deep graph-neighborcoherence preserving network for unsupervised cross-modalhashing, Proc. AAAI Conf. on Artificial Intelligence, VirtualEvent, 35(5), 2021, 4626–4634.
  23. [23] G. Ding, Y. Guo, and J. Zhou, Collective matrix factorizationhashing for multimodal data, Proc. IEEE Conf. on ComputerVision and Pattern Recognition, Columbus, OH, 2014,2083–2090.
  24. [24] M. Long, Y. Cao, J. Wang, and P.S. Yu, Composite correlationquantization for efficient multimodal retrieval, Proc. 39thInternational ACM Sigir Conf. on Research and Developmentin Information Retrieval, Pisa, Italy, 2016, 579–588.
  25. [25] P.-F. Zhang, Y. Li, Z. Huang, and X.-S. Xu, Aggregation-based graph convolutional hashing for unsupervised cross-modal retrieval, IEEE Transactions on Multimedia, 14(8),2021, 466-479.
  26. [26] Z. Yang, X. Deng, L. Guo, and J. Long, Asymmetricsupervised fusion-oriented hashing for cross-modal retrieval,IEEE Transactions on Cybernetics, early access, 2023, 1–14.
  27. [27] K. Simonyan and A. Zisserman, Very deep convolutionalnetworks for large-scale image recognition, Proc. InternationalConf. on Learning Representations, San Diego, CA, USA, 2015,1–15.
  28. [28] X. Song, F. Feng, X. Han, X. Yang, and F. Feng, Neuralcompatibility modeling with attentive knowledge distillation,Proc. ACM/Sigir Proc. 2018, Univ Michigan, Ann Arbor, MI,2018, 5-14.
  29. [29] L. Jing, E. Vahdani, J. Tan, and Y. Tian, Cross-modalcenter loss for 3D cross-modal retrieval, Proc. IEEE Conf.on Computer Vision and Pattern Recognition, Virtual Event,2021, 3142-3151.

Important Links:

Go Back