ASPD-Net: Self-aligned part mask for improving text-based person re-identification with adversarial representation learning

Text-based person re-identification aims to retrieve images of the corresponding person from a large visual database according to a natural language description. When it comes to visual local information extraction, most of the state-of-the-art methods adopt either a strict uniform strategy which ca...

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Veröffentlicht in:Engineering applications of artificial intelligence 2022-11, Vol.116, p.105419, Article 105419
Hauptverfasser: Wang, Zijie, Xue, Jingyi, Wan, Xili, Zhu, Aichun, Li, Yifeng, Zhu, Xiaomei, Hu, Fangqiang
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Sprache:eng
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Zusammenfassung:Text-based person re-identification aims to retrieve images of the corresponding person from a large visual database according to a natural language description. When it comes to visual local information extraction, most of the state-of-the-art methods adopt either a strict uniform strategy which can be too rough to catch local details properly, or pre-processing with external cues which may suffer from the deviations of the pre-trained model and the large computation consumption. In this paper, we proposed an Adversarial Self-aligned Part Detecting Network (ASPD-Net) model which extracts and combines multi-granular visual and textual features. A novel Self-aligned Part Mask Module was presented to autonomously learn the information of human body parts, and obtain visual local features in a soft-attention manner by using K Self-aligned Part Mask Detectors. Regarding the main model branches as a generator, a discriminator is employed to determine whether the representation vector comes from the visual modality or the textual modality. With Adversarial Loss training, ASPD-Net can learn more robust representations, as long as it successfully tricks the discriminator. Experimental results demonstrate that the proposed ASPD-Net outperforms the previous methods and achieves the state-of-the-art performance on the CUHK-PEDES and RSTPReid datasets.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2022.105419