Lifelong Person Search
This addresses the practical need for person search models to handle continuously arriving datasets without forgetting old knowledge, representing an incremental improvement in lifelong learning for a domain-specific task.
The paper tackles the problem of catastrophic forgetting in person search when models are incrementally trained on new datasets, proposing a lifelong learning framework that uses knowledge distillation and rehearsal-based instance matching to preserve old knowledge, achieving significantly superior performance in both detection and re-identification compared to existing methods.
Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, however diverse datasets are continuously given in practical applications of person search. In such cases, they suffer from the catastrophic knowledge forgetting in the old datasets when trained on new datasets. In this paper, we first introduce a novel problem of lifelong person search (LPS) where the model is incrementally trained on the new datasets while preserving the knowledge learned in the old datasets. We propose an end-to-end LPS framework that facilitates the knowledge distillation to enforce the consistency learning between the old and new models by utilizing the prototype features of the foreground persons as well as the hard background proposals in the old domains. Moreover, we also devise the rehearsal-based instance matching to further improve the discrimination ability in the old domains by using the unlabeled person instances additionally. Experimental results demonstrate that the proposed method achieves significantly superior performance of both the detection and re-identification to preserve the knowledge learned in the old domains compared with the existing methods.