Grouped Adaptive Loss Weighting for Person Search

Abstract

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-toend 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. CCS CONCEPTS• Computing methodologies → Computer vision tasks.

Divide and Conquer: Hybrid Pre-training for Person Search

“…Most existing person search methods (Xiao et al 2017;Li et al 2022;Yan et al 2022;Kim et al 2021;Li and Miao 2021;Han, Ko, and Sim 2021;Yan et al 2021Yan et al , 2023Tian et al 2022) use ImageNet pre-trained models (Deng et al 2009), such as ResNet50 (He et al 2016), as the initialization model for feature extraction. However, ImageNet pre-training, which learns classification-related knowledge, is limited in its applicability to downstream tasks, particularly when the target task is significantly different (He, Girshick, and Dollar 2019).…”