ABSTRACT
Domain adaptive image retrieval (DAR) aims to train the model with well-labeled source domain and target images in order to retrieve source instances given query target samples from the identical category space. However, the practical scenario hinders to manually annotate all retrieved images due to huge labeling cost. Motivated by the realistic demand, we firstly define the semi-supervised domain adaptive retrieval (SDAR) problem, assuming the database includes a small proportion annotated source images and abundant unlabeled ones. To overcome the challenging SDAR, this paper propose a novel method named Discriminative Hashing learning (DHLing) which mainly includes two modules, i.e., domain-specific optimization and domain-invariant memory bank. Specifically, the first component explores the structural knowledge of samples to predict the unlabeled images with pseudo labels to achieve hash coding consistency. While, the second one attempts to construct the domain-invariant memory bank to guide the feature generation and achieve cross-domain alignment. Experimental results on several popular cross-domain retrieval benchmarks illustrate the effectiveness of our proposed DHLing on both conventional DAR and new SDAR scenarios by comparing with the state-of-the-art retrieval methods.
- Cong Bai, Ling Huang, Xiang Pan, Jianwei Zheng, and Shengyong Chen. 2018. Optimization of deep convolutional neural network for large scale image retrieval. Neurocomputing, Vol. 303 (2018), 60--67.Google ScholarCross Ref
- Christian Buchta, Martin Kober, Ingo Feinerer, and Kurt Hornik. 2012. Spherical k-means clustering. Journal of statistical software, Vol. 50, 10 (2012), 1--22.Google Scholar
- Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry. 253--262.Google ScholarDigital Library
- Hal Daumé III. 2009. Frustratingly easy domain adaptation. ACL (2009).Google Scholar
- Kun Ding, Bin Fan, Chunlei Huo, Shiming Xiang, and Chunhong Pan. 2016. Cross-modal hashing via rank-order preserving. IEEE Transactions on Multimedia, Vol. 19, 3 (2016), 571--585.Google ScholarDigital Library
- Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014. Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning. PMLR, 647--655.Google Scholar
- Yan Feng, Bin Chen, Tao Dai, and Shu-Tao Xia. 2020. Adversarial attack on deep product quantization network for image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10786--10793.Google ScholarCross Ref
- Bojana Gajic and Ramon Baldrich. 2018. Cross-domain fashion image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1869--1871.Google ScholarCross Ref
- Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2012. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 12 (2012), 2916--2929.Google Scholar
- Albert Gordo, Jon Almazan, Jerome Revaud, and Diane Larlus. 2017. End-to-end learning of deep visual representations for image retrieval. International Journal of Computer Vision, Vol. 124, 2 (2017), 237--254.Google ScholarDigital Library
- Mehrdad Hosseinzadeh and Yang Wang. 2020. Composed query image retrieval using locally bounded features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3596--3605.Google ScholarCross Ref
- Fuxiang Huang, Lei Zhang, Yang Yang, and Xichuan Zhou. 2020. Probability weighted compact feature for domain adaptive retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9582--9591.Google ScholarCross Ref
- Junshi Huang, Rogerio S Feris, Qiang Chen, and Shuicheng Yan. 2015. Cross-domain image retrieval with a dual attribute-aware ranking network. In Proceedings of the IEEE international conference on computer vision. 1062--1070.Google ScholarDigital Library
- Jonathan J. Hull. 1994. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence, Vol. 16, 5 (1994), 550--554.Google ScholarDigital Library
- Xin Ji, Wei Wang, Meihui Zhang, and Yang Yang. 2017. Cross-domain image retrieval with attention modeling. In Proceedings of the 25th ACM international conference on Multimedia. 1654--1662.Google ScholarDigital Library
- Qing-Yuan Jiang and Wu-Jun Li. 2015. Scalable graph hashing with feature transformation.. In IJCAI, Vol. 15. 2248--2254.Google Scholar
- Shuhui Jiang, Yue Wu, and Yun Fu. 2016. Deep bi-directional cross-triplet embedding for cross-domain clothing retrieval. In Proceedings of the 24th ACM international conference on Multimedia. 52--56.Google ScholarDigital Library
- Zhongming Jin, Cheng Li, Yue Lin, and Deng Cai. 2013. Density sensitive hashing. IEEE transactions on cybernetics, Vol. 44, 8 (2013), 1362--1371.Google Scholar
- Taotao Jing, Haifeng Xia, and Zhengming Ding. 2020. Adaptively-accumulated knowledge transfer for partial domain adaptation. In Proceedings of the 28th ACM International Conference on Multimedia. 1606--1614.Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, Vol. 25 (2012), 1097--1105.Google ScholarDigital Library
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE, Vol. 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Xiaodan Liang, Liang Lin, Wei Yang, Ping Luo, Junshi Huang, and Shuicheng Yan. 2016. Clothes co-parsing via joint image segmentation and labeling with application to clothing retrieval. IEEE Transactions on Multimedia, Vol. 18, 6 (2016), 1175--1186.Google ScholarDigital Library
- Jie Lin, Zechao Li, and Jinhui Tang. 2017. Discriminative Deep Hashing for Scalable Face Image Retrieval.. In IJCAI. 2266--2272.Google Scholar
- Kevin Lin, Huei-Fang Yang, Jen-Hao Hsiao, and Chu-Song Chen. 2015. Deep learning of binary hash codes for fast image retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 27--35.Google ScholarCross Ref
- Yen-Yu Lin, Tyng-Luh Liu, and Hwann-Tzong Chen. 2005. Semantic manifold learning for image retrieval. In Proceedings of the 13th annual ACM international conference on Multimedia. 249--258.Google ScholarDigital Library
- Chao Liu, Jingjing Ma, Xu Tang, Fang Liu, Xiangrong Zhang, and Licheng Jiao. 2020. Deep hash learning for remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing (2020).Google Scholar
- Hong Liu, Rongrong Ji, Jingdong Wang, and Chunhua Shen. 2018. Ordinal constraint binary coding for approximate nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, Vol. 41, 4 (2018), 941--955.Google Scholar
- Ji Liu and Lei Zhang. 2019. Optimal projection guided transfer hashing for image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 8754--8761.Google ScholarCross Ref
- Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. 2012. Supervised hashing with kernels. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2074--2081.Google ScholarDigital Library
- Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu. 2013. Transfer feature learning with joint distribution adaptation. In Proceedings of the IEEE international conference on computer vision. 2200--2207.Google ScholarDigital Library
- Xiaoqiang Lu, Yaxiong Chen, and Xuelong Li. 2017. Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE transactions on image processing, Vol. 27, 1 (2017), 106--120.Google Scholar
- Mathias Lux. 2011. Content based image retrieval with lire. In Proceedings of the 19th ACM international conference on Multimedia. 735--738.Google ScholarDigital Library
- Yueming Lv, Wing WY Ng, Ziqian Zeng, Daniel S Yeung, and Patrick PK Chan. 2015. Asymmetric cyclical hashing for large scale image retrieval. IEEE Transactions on Multimedia, Vol. 17, 8 (2015), 1225--1235.Google ScholarDigital Library
- Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro. 2009. Online dictionary learning for sparse coding. In Proceedings of the 26th annual international conference on machine learning. 689--696.Google ScholarDigital Library
- Adnan Qayyum, Syed Muhammad Anwar, Muhammad Awais, and Muhammad Majid. 2017. Medical image retrieval using deep convolutional neural network. Neurocomputing, Vol. 266 (2017), 8--20.Google ScholarDigital Library
- Jerome Revaud, Jon Almazán, Rafael S Rezende, and Cesar Roberto de Souza. 2019. Learning with average precision: Training image retrieval with a listwise loss. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5107--5116.Google ScholarCross Ref
- Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised discrete hashing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 37--45.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Kai Song, Yonghong Tian, Wen Gao, and Tiejun Huang. 2006. Diversifying the image retrieval results. In Proceedings of the 14th ACM international conference on Multimedia. 707--710.Google ScholarDigital Library
- Jun Tang, Ke Wang, and Ling Shao. 2016. Supervised matrix factorization hashing for cross-modal retrieval. IEEE Transactions on Image Processing, Vol. 25, 7 (2016), 3157--3166.Google ScholarDigital Library
- Antonio Torralba and Alexei A Efros. 2011. Unbiased look at dataset bias. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1521--1528.Google ScholarDigital Library
- Yair Weiss, Antonio Torralba, Robert Fergus, et al. 2008. Spectral hashing.. In Advances in neural information processing systems, Vol. 1. 4.Google Scholar
- Dayan Wu, Zheng Lin, Bo Li, Mingzhen Ye, and Weiping Wang. 2017. Deep supervised hashing for multi-label and large-scale image retrieval. In Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. 150--158.Google ScholarDigital Library
- Haifeng Xia and Zhengming Ding. 2020 a. Hgnet: Hybrid generative network for zero-shot domain adaptation. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII 16. Springer, 55--70.Google ScholarCross Ref
- Haifeng Xia and Zhengming Ding. 2020 b. Structure preserving generative cross-domain learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4364--4373.Google ScholarCross Ref
- Liang Xie, Jialie Shen, and Lei Zhu. 2016. Online cross-modal hashing for web image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.Google ScholarCross Ref
- Chenggang Yan, Biao Gong, Yuxuan Wei, and Yue Gao. 2020. Deep multi-view enhancement hashing for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).Google ScholarCross Ref
- Guanglei Yang, Haifeng Xia, Mingli Ding, and Zhengming Ding. 2020. Bi-directional generation for unsupervised domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 6615--6622.Google ScholarCross Ref
- Huei-Fang Yang, Kevin Lin, and Chu-Song Chen. 2017. Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 2 (2017), 437--451.Google Scholar
- Meng Yang, Lei Zhang, Xiangchu Feng, and David Zhang. 2011. Fisher discrimination dictionary learning for sparse representation. In 2011 International Conference on Computer Vision. IEEE, 543--550.Google ScholarDigital Library
- Tao Yao, Gang Wang, Lianshan Yan, Xiangwei Kong, Qingtang Su, Caiming Zhang, and Qi Tian. 2019. Online latent semantic hashing for cross-media retrieval. Pattern Recognition, Vol. 89 (2019), 1--11.Google ScholarCross Ref
- Xiangtao Zheng, Yichao Zhang, and Xiaoqiang Lu. 2020. Deep balanced discrete hashing for image retrieval. Neurocomputing, Vol. 403 (2020), 224--236.Google ScholarCross Ref
- Joey Tianyi Zhou, Heng Zhao, Xi Peng, Meng Fang, Zheng Qin, and Rick Siow Mong Goh. 2018. Transfer hashing: From shallow to deep. IEEE transactions on neural networks and learning systems, Vol. 29, 12 (2018), 6191--6201.Google Scholar
Index Terms
- Semi-supervised Domain Adaptive Retrieval via Discriminative Hashing Learning
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