This article discusses a new approach to nighttime person re-identification (ReID) using a Collaborative Enhancement Network (CENet). The authors point out that current methods for nighttime ReID often rely on the combination of relighting networks and ReID networks in a sequential manner, which can limit the ReID performance and neglect the collaborative modeling between relighting and ReID tasks.

CENet: A Parallel Transformer Network

To address these issues, the authors propose CENet, which is a parallel Transformer network. The parallel structure of CENet allows for effective multilevel feature interactions without being influenced by the quality of relighting images. By avoiding the sequential nature of traditional methods, CENet can improve the ReID performance.

The authors further enhance the collaborative modeling between image relighting and person ReID tasks by integrating multilevel feature interactions in CENet. They achieve this by sharing the Transformer encoder to build low-level feature interactions and performing feature distillation to transfer high-level features from image relighting to ReID. This approach ensures a comprehensive collaboration between the two tasks and enhances the overall performance of the system.

Multi-Domain Learning Algorithm

In addition to addressing the limitations of previous methods, the authors also consider the challenge of limited real-world nighttime person ReID datasets and the domain gap between synthetic and real-world data. To overcome these challenges, they propose a multi-domain learning algorithm for training CENet.

This algorithm alternately utilizes both small-scale real-world datasets and large-scale synthetic datasets to reduce the inter-domain difference and improve the performance of CENet on real nighttime datasets.

Experimental Validation

To demonstrate the effectiveness of CENet, extensive experiments are conducted on two real nighttime datasets: Night600 and RGBNT201_rgb, as well as a synthetic nighttime ReID dataset. These experiments show that CENet outperforms existing methods and achieves state-of-the-art results.

The authors also highlight their intention to release the code and synthetic dataset, which will enable further research and development in nighttime person ReID.

Overall, this article presents an innovative approach to nighttime person re-identification using the Collaborative Enhancement Network (CENet). By addressing the limitations of existing methods and leveraging multilevel feature interactions and a multi-domain learning algorithm, CENet demonstrates improved performance in real-world nighttime scenarios. This research opens up avenues for further exploration in the field of ReID and provides valuable resources for the development of more effective nighttime person re-identification systems.

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