Name
Title | ||
|---|---|---|
Img-Net: Inner-Cross-Modal Attentional Multigranular Network For Description-Based Person Re-Identification |
Abstract | ||
|---|---|---|
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. (C) 2020 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1117/1.JEI.29.4.043028 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | DocType | Volume |
person re-identification, natural language description, multigranular matching | Journal | 29 |
Issue | ISSN | Citations |
4 | 1017-9909 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijie Wang | 1 | 0 | 2.03 |
| Aichun Zhu | 2 | 16 | 8.10 |
| Zhe Zheng | 3 | 0 | 0.34 |
| Jing Jin | 4 | 1 | 1.77 |
| Zhouxin Xue | 5 | 0 | 0.34 |
| Gang Hua | 6 | 15 | 6.56 |