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Learning discriminative representations for unseen person images is critical for person re-identification (ReID). Most of the current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation learning procedure named part loss network, to minimize both the empirical classification risk on training person images and the representation learning risk on unseen person images. The representation learning risk is evaluated by the proposed part loss, which automatically detects human body parts and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering part loss enforces the deep network to learn representations for different body parts and gain the discriminative power on unseen persons. Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that our representation outperforms existing deep representations. [ABSTRACT FROM AUTHOR]

• We propose a lightweight attention mechanism to exploit diverse parts of human bodies for addressing visual variations. • We propose an effective framework for video-based person re-identification. • We conduct extensive experiments on three popular benchmarks for demonstrating the effectiveness of our proposed method. Attention mechanisms have achieved success in video-based person re-identification (re-ID). However, current global attentions tend to focus on the most salient parts, e.g., clothes, and ignore other subtle but valuable cues, e.g., hair, bag, and shoes. They still do not make full use of valuable information from diverse parts of human bodies. To tackle this issue, we propose a Diverse Part Attentive Network (DPAN) to exploit discriminative and diverse body cues. The framework consists of two modules: spatial diverse part attention and temporal diverse part attention. The spatial module utilizes channel grouping to exploit diverse parts of human bodies including salient and subtle parts. The temporal module aims to learn diverse weights for fusing learned features. Besides, this framework is lightweight, which introduces marginal parameters and computational complexities. Extensive experiments were conducted on three popular benchmarks, i.e. iLIDS-VID, PRID2011 and MARS. Our method achieves competitive performance on these datasets compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• We decouple the difficulties affecting the person re-identification task into the Camera-Camera (CC) problem and the Camera-Person (CP) problem. • We propose a bi-stream generative model for solving the CC and CP problems separately, with promising results. • We design a part-weighted loss based on the unbalanced number of human body parts in the dataset to guide the model to focus on the more important parts. Generalizable person re-identification (re-ID) has attracted growing attention due to its powerful adaptation capability in the unseen data domain. However, existing solutions often neglect either crossing cameras (e.g., illumination and resolution differences) or pedestrian misalignments (e.g., viewpoint and pose discrepancies), which easily leads to poor generalization capability when adapted to the new domain. In this paper, we formulate these difficulties as: 1) Camera-Camera (CC) problem, which denotes the various human appearance changes caused by different cameras; 2) Camera-Person (CP) problem, which indicates the pedestrian misalignments caused by the same identity person under different camera viewpoints or changing pose. To solve the above issues, we propose a Bi-stream Generative Model (BGM) to learn the fine-grained representations fused with camera-invariant global feature and pedestrian-aligned local feature, which contains an encoding network and two stream decoding sub-network. Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors. For the CP problem, another stream learns a pedestrian-aligned local feature for pedestrian alignment using information-complete densely semantically aligned part maps. Moreover, a part-weighted loss function is presented to reduce the influence of missing parts on pedestrian alignment. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on the large-scale generalizable re-ID benchmarks, involving domain generalization setting and cross-domain setting. [ABSTRACT FROM AUTHOR]