利用类别信息和列表排序的跨模态检索

doi:10.3778/j.issn.1673-9418.2004068

摘要/Abstract

摘要：

随着越来越多多模态数据的出现，跨模态检索引起了广泛的关注。跨模态检索面临一大挑战为模态鸿沟，为了解决数据的异构性问题，公共子空间学习的方法被提出。然而，大部分方法仅仅是单独考虑了样本之间的相关联信息或不相关信息，而没有同时考虑样本间的相关信息和不相关信息。除此之外，大部分方法对于样本之间相似度的比较，使用的是基于文档对的排序比较，其没有充分考虑样本之间的类内依赖性和类间样本的结构差异性。基于此，提出了一种同时而不是单独考虑样本间的类内关系和类间关系的基于列表排序的跨模态检索方法，其通过列表排序最大化锚点与正样本之间的相似性，同时最小化锚点和负样本间的相似性。实验结果验证了该算法在跨模态检索中的有效性。

关键词: 相关信息, 不相关信息, 类内, 类间, 列表排序

Abstract:

Cross-modal retrieval has attracted significant attention due to the increasing use of multi-modal data. A major challenge for cross-modal retrieval is the modal gap. To cope with the heterogeneity, common subspace learning method is proposed. However, most of them mainly focus on relevant or irrelevant information, and do not consider the relevant and irrelevant information simultaneously. In addition, there are many pairwise methods for cross-modal retrieval, but they do not consider the internal dependencies between the doc pairs corresponding to the same query and do not make full use of the structure between the samples. To take full account of the intra-class and inter-class relationships between samples, the cross-modal retrieval by listwise ranking and class information (C2MLR2) is proposed, which maximizes the similarity of positive samples to the anchor and meanwhile minimizes the similarity of the negative samples to the anchor via listwise ranking. Experimental results verify the effectiveness of the algorithm in cross-modal retrieval.

Key words: relevant information, irrelevant information, intra-class, inter-class, listwise ranking

刘雨萍, 葛红, 曾奕斌. 利用类别信息和列表排序的跨模态检索[J]. 计算机科学与探索, 2021, 15(3): 506-514.

LIU Yuping, GE Hong, ZENG Yibin. Cross-Modal Retrieval by Class Information and Listwise Ranking[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(3): 506-514.

参考文献

[1] XU M, ZHU Z, ZHAO Y, et al. Subspace learning by kernel dependence maximization for cross-modal retrieval[J]. Neuro-computing, 2018, 309: 94-105.
[2] CAO G Q, WARIS M A, IOSIFIDIS A, et al. Multi-modal subspace learning with dropout regularization for cross-modal recognition and retrieval[C]//Proceedings of the 2016 6th International Conference on Image Processing Theory, Tools and Applications,?Oulu, Dec 12-15, 2016. Piscataway: IEEE, 2016: 1-6.
[3] HE R, ZHANG M, WANG L, et al. Cross-modal learning via pairwise constraints[J]. IEEE Transactions on Image Pro-cessing, 2014, 24(12): 5543-5556.
[4] HARDOON D R, SZEDMAK S, SHAWE-TAYLOR J. Can-onical correlation analysis: an overview with application to learning methods[J]. Neural Computation, 2004, 16(12): 2639-2664.
[5] ANDREW G, ARORA R, BILMES J A, et al. Deep can-onical correlation analysis[C]//Proceedings of the 30th Inter-national Conference on Machine Learning, Atlanta, Jun 16-21, 2013: 1247-1255.
[6] ROSIPAL R, KR?MER N. Overview and recent advances in partial least squares[C]//LNCS 3940: Proceedings of the Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, Bohinj, Feb 23-25,?2005. Berlin, Heidelberg: Springer, 2005: 34-51.
[7] SHARMA A, KUMAR A, DAUMé H, et al. Generalized multiview analysis: a discriminative latent space[C]//Pro-ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, Jun 16-21, 2012. Wash-ington: IEEE Computer Society, 2012: 2160-2167.
[8] WANG K Y, HE R, WANG W, et al. Learning coupled feature spaces for cross-modal matching[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Dec 1-8, 2013. Washington: IEEE Computer Society, 2013: 2088-2095.
[9] QU G Z, XIAO J, ZHU J, et al. Cross-modal learning to rank with adaptive listwise constraint[C]//Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Apr 15-20, 2018. Piscataway: IEEE, 2018: 1658-1662.
[10] XU Q Z, LI M, YU M J. Learning to rank with relational graph and pointwise constraint for cross-modal retrieval[J]. Soft Computing, 2019, 23(19): 9413-9427.
[11] FROME A, CORRADO G S, SHLENS J, et al. DeViSE: a deep visual-semantic embedding model[C]//Proceedings of the 27th Annual Conference on Neural Information Process-ing Systems, Lake Tahoe, Dec 5-10, 2013. Red Hook: Curran Associates, 2013: 2121-2129.
[12] JIAN Y W, XIAO J, CAO Y, et al. Deep pairwise ranking with multi-label information for cross-modal retrieval[C]// Proceedings of the 2019 IEEE International Conference on Multimedia and Expo, Shanghai, Jul 8-12, 2019. Piscataway: IEEE,?2019: 1810-1815.
[13] YUAN Z Q, SANG J T, LIU Y, et al. Latent feature learning in social media network[C]//Proceedings of the 2013 ACM Multimedia Conference, Barcelona, Oct 21-25, 2013. New York: ACM, 2013: 253-262.
[14] WANG J, HE Y H, KANG C C, et al. Image-text cross-modal retrieval via modality-specific feature learning[C]// Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, Shanghai, Jun 23-26, 2015. New York: ACM, 2015: 347-354.
[15] JI Z, WANG H R, HAN J G, et al. Saliency-guided atten-tion network for image-sentence matching[C]//Proceedings of the 2019 IEEE/CVF International Conference on Com-puter Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 5754-5763.
[16] JI Z, LIN Z G, WANG H R, et al. Multi-modal memory enhancement attention network for image-text matching[J]. IEEE Access, 2020, 8: 38438-38447.
[17] WANG H R, JI Z, LIN Z G, et al. Stacked squeeze and excitation recurrent residual network for visual semantic matching[J]. Pattern Recognition, 2020, 105: 107359.
[18] GU W, GU X Y, GU J Z, et al. Adversary guided asymme-tric Hashing for cross-modal retrieval[C]//Proceedings of the 2019 International Conference on Multimedia Retrieval, Ottawa, Jun 10-13, 2019. New York: ACM, 2019: 159-167.
[19] LI K, DING Z M, LI K P, et al. Support neighbor loss for person re-identification[C]//Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Oct 22-26, 2018. New York: ACM, 2018: 1492-1500.
[20] ZHENG Z D, ZHENG L, YANG Y. A discriminatively learned CNN embedding for person reidentification[J]. ACM Tran-sactions on Multimedia Computing, Communications, and Applications, 2017, 14(1): 1-20.
[21] RASIWASIA N, COSTA PEREIRA J, COVIELLO E, et al. A new approach to cross-modal multimedia retrieval[C]// Proceedings of the 18th ACM International Conference on Multimedia, Firenze, Oct 25-29, 2010. New York: ACM, 2010: 251-260.
[22] JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe: con-volutional architecture for fast feature embedding[C]//Pro-ceedings of the 22nd ACM International Conference on Multimedia, Orlando, Nov 3-7, 2014. New York: ACM, 2014: 675-678.
[23] HWANG S J, GRAUMAN K. Reading between the lines: object localization using implicit cues from image tags[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 2011, 34(6): 1145-1158.
[24] RANJAN V, RASIWASIA N, JAWAHAR C V. Multi-label cross-modal retrieval[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Dec 7-13, 2015. Piscataway: IEEE, 2015: 4094-4102.
[25] KANG C, XIANG S, LIAO S, et al. Learning consistent feature representation for cross-modal multimedia retrieval[J]. IEEE Transactions on Multimedia, 2015, 17(3): 370-381.
[26] PENG Y, HUANG X, QI J. Cross-media shared representa-tion by hierarchical learning with multiple deep networks[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, Jul 9-15, 2016. Palo Alto: AAAI Press, 2016: 3846-3853.
[27] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[28] YAN S, XU D, ZHANG B, et al. Graph embedding and ex-tensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 29(1): 40-51.
[29] ZHANG L, MA B, LI G, et al. Metric based on multi-order spaces for cross-modal retrieval[C]//Proceedings of the 2017 IEEE International Conference on Multimedia and Expo, Hong Kong, Jul 10-14, 2017. Piscataway: IEEE, 2017: 1374-1379.
[30] WU F, JIANG X, LI X, et al. Cross-modal learning to rank via latent joint representation[J]. IEEE Transactions on Image Processing, 2015, 24(5): 1497-1509.