Tabular text embedding for Vietnamese text-based person search

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Abstract

Vietnamese text-based person search is still a challenging problem with the limited dataset of Vietnamese descriptions. The current popular approach to this problem is Deep Neural Networks (DNNs), and recently, transformer networks have been more favored because of their outperformance over CNN and RNN networks for both vision and natural language processing tasks. However, DNN, or transformer networks, require a large amount of training data and computing time for efficient learning of visual and textual features. This brings a burden for implementing Vietnamese text-based person search by DNN, or transformer networks. Towards building a Vietnamese text-based person search system on a scarce resource dataset of Vietnamese descriptive sentences with low computing cost, in this work, we propose to apply the transformer-based architecture named TabTransformer for contextual embedding of the noun phrases chunked from the Vietnamese descriptive sentences. This is the first time the TabTransformer network has been deployed together with CNN and RNN architectures for Vietnamese text-based person search. The experimental results on a limited dataset of 3000VnPersonSearch show the better recognition accuracy of the proposed method compared to the baseline method by about 7.5% at Rank 1. In addition, the computing time of our method is more effective than the baseline method.