Qiqi Li¹, Longfei Ma¹, Zheng Jiang¹, Mingyong Li^1,*, Bo Jin²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3713-3728, 2023, DOI:10.32604/cmc.2023.037463 - 31 March 2023

Abstract In recent years, cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage. Cross-modal retrieval technology can be applied to search engines, cross-modal medical processing, etc. The existing main method is to use a multi-label matching paradigm to finish the retrieval tasks. However, such methods do not use fine-grained information in the multi-modal data, which may lead to sub-optimal results. To avoid cross-modal matching turning into label matching, this paper proposes an end-to-end fine-grained cross-modal hash retrieval method, which can focus more on the fine-grained semantic… More >

Mingyong Li, Qiqi Li, Zheng Jiang, Yan Ma^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1401-1414, 2023, DOI:10.32604/csse.2023.034757 - 09 February 2023

Abstract In recent years, the development of deep learning has further improved hash retrieval technology. Most of the existing hashing methods currently use Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process image and text information, respectively. This makes images or texts subject to local constraints, and inherent label matching cannot capture fine-grained information, often leading to suboptimal results. Driven by the development of the transformer model, we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs. Specifically, we use a More >

Zhiyi Li^1,2,*, Xiaomian Xu², Du Zhang¹, Peng Zhang²

Computer Systems Science and Engineering, Vol.36, No.2, pp. 383-405, 2021, DOI:10.32604/csse.2021.014563 - 05 January 2021

Abstract In the era of big data rich in We Media, the single mode retrieval system has been unable to meet people’s demand for information retrieval. This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes: A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network (CMHR-DRN). The model construction is divided into two stages: The first stage is the feature extraction of different modal data, including the use of Deep Residual Network (DRN) to extract the image features, using the method of combining TF-IDF with the full… More >