Autoencoder-based self-supervised hashing for cross-modal retrieval
Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the het...
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| Veröffentlicht in: | Multimedia tools and applications Jg. 80; H. 11; S. 17257 - 17274 |
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| Abstract | Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability. |
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| AbstractList | Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability. |
| Author | Zhang, Jiajia Li, Yifan Luo, Xuan Cui, Lei Huang, Chengkai Wang, Xuan Qi, Shuhan |
| Author_xml | – sequence: 1 givenname: Yifan surname: Li fullname: Li, Yifan organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 2 givenname: Xuan surname: Wang fullname: Wang, Xuan organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 3 givenname: Lei surname: Cui fullname: Cui, Lei organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 4 givenname: Jiajia surname: Zhang fullname: Zhang, Jiajia organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 5 givenname: Chengkai surname: Huang fullname: Huang, Chengkai organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 6 givenname: Xuan surname: Luo fullname: Luo, Xuan organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) – sequence: 7 givenname: Shuhan surname: Qi fullname: Qi, Shuhan email: shuhanqi@cs.hitsz.edu.cn organization: Computer Science and Technology, Harbin Institute of Technology (Shenzhen) |
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| Cites_doi | 10.1109/TCSVT.2013.2276704 10.1145/2911996.2912000 10.24963/ijcai.2018/158 10.1145/1646396.1646452 10.1609/aaai.v31i1.10719 10.1109/CVPR.2015.7298654 10.1109/CVPR.2015.7299011 10.1609/aaai.v33i01.33014400 10.1109/CVPR.2012.6247923 10.1109/ICCV.2017.226 10.1609/aaai.v27i1.8464 10.5244/C.28.6 10.24963/ijcai.2018/85 10.1093/biomet/28.3-4.321 10.1145/1460096.1460104 10.1007/978-3-319-54181-5_5 10.1609/aaai.v28i1.8995 10.1109/CVPR.2019.00202 10.1109/CVPR.2015.7298947 10.1109/TMM.2018.2856090 10.1016/j.jvcir.2018.12.025 10.1109/TIP.2018.2821921 10.1109/TPAMI.2015.2505311 10.1145/3126686.3126723 10.1109/TIP.2016.2607421 10.1145/1873951.1873987 10.1109/CVPR.2016.641 10.1109/CVPR.2017.348 10.1145/1390156.1390285 10.1109/TIP.2018.2890144 10.1145/2600428.2609610 10.1145/3240508.3240684 10.1109/TCSVT.2015.2400779 |
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| DOI | 10.1007/s11042-020-09599-7 |
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| Keywords | Self-supervised Cross-modal retrieval Autoencoder Hash learning |
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| References_xml | – reference: Kumar S, Udupa R (2011) Learning hash functions for cross-view similarity search. In: International Joint Conference on Artificial Intelligence. AAAI Press, pp 1360–1365 – reference: Wang X, Shi Y, Kitani K M (2016) Deep supervised hashing with triplet labels. In: Proceedings of Asian conference on computer vision, vol 10111 LNCS. Springer, Cham, pp 70–84 – reference: Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceeding of the ACM International Conference on Image and Video Retrieval - CIVR ’09 ACM Press New York, New York, USA 1 – reference: Zhang C, Peng Y (2018) Better and faster: Knowledge transfer from multiple self-supervised learning tasks via graph distillation for video classification. In: IJCAI International Joint Conference on Artificial Intelligence, vol 2018-July, pp 1135–1141 – reference: Doersch C, Zisserman A, Deepmind (2017) Multi-task Self-Supervised Visual Learning. In: Proceedings of the IEEE international conference on computer vision, pp 2070–2079 – reference: LiuXYuGDomeniconiCWangJRenYGuoMRanking-Based Deep Cross-Modal HashingProceedings of the AAAI Conference on Artificial Intelligence2019334400440710.1609/aaai.v33i01.33014400 – reference: Zhuang B, Lin G, Shen C, Reid I (2016) Fast training of triplet-based deep binary embedding networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2016-Decem, pp 5955–5964 – reference: WangKHeRWangLWangWTanTJoint Feature Selection and Subspace Learning for Cross-Modal RetrievalIEEE Transactions on Pattern Analysis and Machine Intelligence201638102010202310.1109/TPAMI.2015.2505311 – reference: ZhaiXPengYXiaoJLearning cross-media joint representation with sparse and semisupervised regularizationIEEE Transactions on Circuits and Systems for Video Technology201424696597810.1109/TCSVT.2013.2276704 – reference: Yang E, Deng C, Liu W, Liu X, Tao D, Gao X (2017) Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1618–1625 – reference: Jiang Q-Y, Li W-J (2017) Deep Cross-Modal Hashing. 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