  Person search is an integrated task of multiple sub-tasks such as
foreground/background classification, bounding box regression and person
re-identification. Therefore, person search is a typical multi-task learning
problem, especially when solved in an end-to-end manner. Recently, some works
enhance person search features by exploiting various auxiliary information,
e.g. person joint keypoints, body part position, attributes, etc., which brings
in more tasks and further complexifies a person search model. The inconsistent
convergence rate of each task could potentially harm the model optimization. A
straightforward solution is to manually assign different weights to different
tasks, compensating for the diverse convergence rates. However, given the
special case of person search, i.e. with a large number of tasks, it is
impractical to weight the tasks manually. To this end, we propose a Grouped
Adaptive Loss Weighting (GALW) method which adjusts the weight of each task
automatically and dynamically. Specifically, we group tasks according to their
convergence rates. Tasks within the same group share the same learnable weight,
which is dynamically assigned by considering the loss uncertainty. Experimental
results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the
effectiveness of our method.
