Decoupling End-to-End Person Search for Optimal Performance

The author proposes a fully decoupled end-to-end person search model to optimize performance by separating detection and re-identification tasks. The task-incremental person search network enables independent learning for conflicting objectives, achieving the best results for both sub-tasks.