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Multiple Feature Subspaces Based Multi-Task Learning for Person Re-Identification

Person re-identification(Re-ID) is an important task with the purpose of retrieving the person with the same identity across multiple cameras. Multi-task learning(MTL) is a useful learning paradigm in machine learning, and the aim is to leverage useful information included in multiple related tasks to help improve the generalization performance of all the tasks. So the multi-task learning may be an effective way to improve the performance of a model. Existing works in person Re-ID using multi-task learning often require the use of additional information or the design of complex multiple tasks. This paper presents a new simple multi-task learning method for Person Re-ID, which is based on multiple feature subspaces. The idea of this method is to regard the training of each feature subspace as a task in multi-task learning, and we can improve the performance by jointly training multiple feature subspaces. Our method is simple and effective compared to other methods because the feature subspace is easy to obtain. Experiments show that the proposed method can significantly improve performance for person Re-ID.