Digital Library[ Search Result ]
Model Contrastive Federated Learning on Re-Identification
Seongyoon Kim, Woojin Chung, Sungwoo Cho, Yongjin Yang, Shinhyeok Hwang, Se-Young Yun
http://doi.org/10.5626/JOK.2024.51.9.841
Advances in data collection and computing power have dramatically increased the integration of AI technology into various services. Traditional centralized cloud data processing raises concerns over the exposure of sensitive user data. To address these issues, federated learning (FL) has emerged as a decentralized training method where clients train models locally on their data and send locally updated models to a central server. The central server aggregates these locally updated models to improve a global model without directly accessing local data, thereby enhancing data privacy. This paper presents FedCON, a novel FL framework specifically designed for re-identification (Re-ID) tasks across various domains. FedCON integrates contrastive learning with FL to enhance feature representation, which is crucial for Re-ID tasks that emphasize similarity between feature vectors to match identities across different images. By focusing on feature similarity, FedCON can effectively addresses data heterogeneity challenges and improve the global model's performance in Re-ID applications. Empirical studies on person and vehicle Re-ID datasets demonstrated that FedCON outperformed existing FL methods for Re-ID. Our experiments with FedCON on various CCTV datasets for person Re-ID showed superior performance to several baselines. Additionally, FedCON significantly enhanced vehicle Re-ID performance on real-world datasets such as VeRi-776 and VRIC, demonstrating its practical applicability.
Person Re-Identification Using an Attention Pyramid for Local Multiscale Feature Embedding Extracted from a Person’s Image
http://doi.org/10.5626/JOK.2021.48.12.1305
In this paper, a person re-identification scheme using the dual pyramid adapting attention mechanisms to extract more elaborate local feature embedding by excluding the noises caused by the unnecessary backgrounds in person’s image is proposed. With the dual pyramid of local and scale ones, the spatial attention is used to suppress the noise effects caused by unnecessary backgrounds, and the channel attention is used to emphasize the relatively important multiscale features when the local feature embedding is constructed. In the experiments, the proposed scheme was compared with other cases in which the attention module is not used for each pyramid to confirm the optimal configuration and compared based on the rank-1 accuracy with the state-of-the-art studies for the person re-identification. According to the experimental results, the proposed method showed a maximum rank-1 accuracy of 99.4%, which is higher by at least about 0.2% and at most by about 13.8% than previous works.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr