Mr. Zijie Wang Profile

Zijie Wang

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SPIE Journal Paper | 28 August 2020

IMG-Net: inner-cross-modal attentional multigranular network for description-based person re-identification

Zijie Wang, Aichun Zhu, Zhe Zheng, Jing Jin, Zhouxin Xue, Gang Hua

JEI, Vol. 29, Issue 04, 043028, (August 2020) https://doi.org/10.1117/12.10.1117/1.JEI.29.4.043028

KEYWORDS: Visualization, Feature extraction, Information visualization, Performance modeling, Statistical analysis, Databases, Convolution, Visual process modeling, Video surveillance, Neural networks

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Given a natural language description, description-based person re-identification aims to retrieve images of the matched person from a large-scale visual database. Due to the existing modality heterogeneity, it is challenging to measure the cross-modal similarity between images and text descriptions. Many of the existing approaches usually utilize a deep-learning model to encode local and global fine-grained features with a strict uniform partition strategy. This breaks the part coherence, making it difficult to capture meaningful information from the within-part and semantic information among body parts. To address this issue, we proposed an inner-cross-modal attentional multigranular network (IMG-Net) to incorporate inner-modal self-attention and cross-modal hard-region attention with the fine-grained model for extracting the multigranular semantic information. Specifically, the inner-modal self-attention module is proposed to address the within-part consistency broken problem using both spatial-wise and channel-wise information. Following it is a multigranular feature extraction module, which is used to extract rich local and global visual and textual features with the help of group normalization (GN). Then a cross-modal hard-region attention module is proposed to obtain the local visual representation and phrase representation. Furthermore, a GN is used instead of batch normalization for the accurate batch statistics estimation. Comprehensive experiments with ablation analysis demonstrate that IMG-Net achieves the state-of-the-art performance on the CUHK-PEDES dataset and outperforms other previous methods significantly.

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