Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer

By Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, Nenghai Yu. Computer Vision and Pattern Recognition, 2020

349 citations recorded by the source corpus.

Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. Existing works mainly focus on learning modality-shared representation by embedding different modalities into a same feature space, lowering the upper bound of feature distinctiveness. In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the reidentification performance. We model the affinities of different modality samples according to the shared features and then transfer both shared and specific features among and across modalities. We also propose a complementary feature learning strategy including modality adaption, project adversarial learning and reconstruction enhancement to learn discriminative and complementary shared and specific features of each modality, respectively. The entire cmSSFTalgorithm can be trained in an end-to-end manner. We conducted comprehensive experiments to validate the superiority ofthe overall algorithm and the effectiveness ofeach component. The proposed algorithm significantly outperforms state-of-the-arts by 22.5% and 19.3% mAP on the two mainstream benchmark datasets SYSU-MM01 and RegDB, respectively.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/52ee037195398c596e7548a5f6c224250be55b40

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yang Yang
2. Guan'an Wang
3. Bo Du
4. Erjin Zhou
5. Zhicheng Wang
6. Gang Yu
7. Mang Ye

In Computer Vision and Pattern Recognition

1. Bo Du
2. Mang Ye
3. Mike Zheng Shou
4. Huanyu Liu
5. Yang Yang
6. Guan'an Wang
7. Weijian Ruan
8. Erjin Zhou

Citations in the corpus

• Channel Augmented Joint Learning for Visible-Infrared Recognition (2021, methodology): “Different from the widely-used bidirectional triplet metric learning [7, 24, 49], we directly optimize the feature embeddings in a mixed batch with the same identity classifier and metric for original visible, infrared and channel augmented modalities.”
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification (2020, methodology): “Existing methods can be grouped into hand-crafted descriptors [25, 39, 18], metric learning methods [44, 16, 19] and deep learning algorithms [43, 11, 35, 36, 37, 22].”
• Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer (2021, neutral): “Recently, person Re-ID has drawn a growing amount of interest from academia and industry, and various methods have been proposed [20, 24, 14, 38, 18, 27, 42, 43].”