Multitask Person Re-Identification using Homoscedastic Uncertainty Learning

In this paper, we propose a new multitask neural network called Part Attribute Loss Net (PALNet) for person re-identification (re-id) with homoscedastic uncertainty learning. Currently, many person re-id algorithms use person identity as the main ground truth information to train and to perform prediction task. This single task approach is simple to setup but usually provides poor generalization performance. Some other person re-id works [1] [2] incorporate additional cues such as body parts to improve the learned representations. However, this requires additional body part annotations. Person attributes, which are present in some of the large person re-id datasets like Market1501 [3] and DukeMTMC-reID [4], are not used. Furthermore, the multitask networks found in some of the recent works use heuristic approach in weighing their task losses. Our PALNet seeks to address these issues by leveraging on person identity classification, body part detection and person attribute prediction, all built into a unified network. We also incorporate the homoscedastic uncertainty learning to automatically determine the weighing of the loss function of the different tasks. The PALNet consists of three tasks, with person identity classification as the main task. The body part detection and person attributes are two sub tasks to share different learning experience with the main task. The uncertainty learning provide us good indication of the observable task noises and allows us to optimize the accuracy performance without the time consuming grid search method. When benchmarking on DukeMTMC-reID dataset [4], our approach outperforms other state-of-the-art methods. On Market1501 dataset [3], we are on par with the best state-of-the-art method but outperforms the rest by at least 6.4% mAP and 3.5% Rank-1 accuracy. These results are reported without re-ranking.