This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts or human pose to improve the re-ID performance. Attribute information, however, is often not utilized. The proposed AANet leverages on a baseline model that uses body parts and integrates the key attribute information in an unified learning framework. The AANet consists of a global person ID task, a part detection task and a crucial attribute detection task. By estimating the class responses of individual attributes and combining them to form the attribute attention map (AAM), a very strong discriminatory representation is constructed. The proposed AANet outperforms the best state-of-the-art method \cite{Sun_2018_ECCV} using ResNet-50 by 3.36\% in mAP and 3.12\% in Rank-1 accuracy on DukeMTMC-reID dataset. On Market1501 dataset, AANet achieves 92.38\% mAP and 95.10\% Rank-1 accuracy with re-ranking, outperforming~\cite{kalayeh2018human}, another state of the art method using ResNet-152, by 1.42\% in mAP and 0.47\% in Rank-1 accuracy. In addition, AANet can perform person attribute prediction (e.g., gender, hair length, clothing length etc.), and localize the attributes in the query image.

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.