Self-Training Based Semi-Supervised and Semi-Paired Hashing Cross-Modal Retrieval
Rongrong Jing1,2; Hu Tian1,2; Xingwei Zhang1,2; Gang Zhou1,2; Xiaolong Zheng1,2; Dajun Zeng1,2
2022-07
Conference NameThe 2022 International Joint Conference on Neural Networks (IJCNN)
Conference Date2022-07
Conference PlacePadua, Italy
Abstract

The aim of cross-modal retrieval is to search for flexible results across different types of multimedia data. However, the labeled data is usually limited and not well paired with different modalities in practical applications. These issues are not well addressed in the existing works, which cannot consider the semantic information about unlabeled and unpaired data, synchronously. Self-training is a well-known strategy to handle semi-supervised problems. Motivated by the self-training, this paper proposes a self-training-based cross-modal hashing framework (STCH) to tackle the semi-supervised and semi-paired challenges. In the framework, graph neural networks are used to capture potential intra-modality and inter-modality similarities to produce pseudo labels. Then the inconsistent pseudo labels of different modalities are refined with a heuristic filter to enhance the model robustness. To train STCH, we propose an alternating learning strategy to conduct the self-train by predicting pseudo labels during the training procedure, which can be seamlessly incorporated into semi-supervised and supervised learning. In this way, the proposed method can leverage sufficient semantic information to enhance the semi-supervised effect and address the semi-paired problem. Experiments on the real-world datasets demonstrate that our approach outperforms related methods on hash cross-modal retrieval.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48818
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorXiaolong Zheng
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Rongrong Jing,Hu Tian,Xingwei Zhang,et al. Self-Training Based Semi-Supervised and Semi-Paired Hashing Cross-Modal Retrieval[C],2022.
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