Person re-identification (reID) is an important task that requires to retrieve a person's images from an image dataset, given one image of the person of interest. For learning robust person features, the pose variation of person images is one of the key challenges. Existing works targeting the problem either perform human alignment, or learn human-region-based representations. Extra pose information and computational cost is generally required for inference. To solve this issue, a Feature Distilling Generative Adversarial Network (FD-GAN) is proposed for learning identity-related and pose-unrelated representations. It is a novel framework based on a Siamese structure with multiple novel discriminators on human poses and identities. In addition to the discriminators, a novel same-pose loss is also integrated, which requires appearance of a same person's generated images to be similar. After learning pose-unrelated person features with pose guidance, no auxiliary pose information and additional computational cost is required during testing. Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Person reID @cite_39 @cite_42 @cite_12 @cite_37 @cite_9 @cite_41 @cite_16 @cite_36 is a challenging task due to various human poses, domain differences, occlusions, . Two main types of methods were adopted in previous works, learning discriminative person representations @cite_40 @cite_28 @cite_0 and metric learning @cite_37 @cite_35 @cite_45 @cite_23 . PAN @cite_7 aligns pedestrians and learn person features simultaneously without any extra annotation. Zhao al @cite_18 proposed SpindleNet for learning person features of different body regions with additional human pose information. Most recent methods @cite_7 @cite_37 @cite_35 @cite_45 @cite_23 @cite_47 @cite_24 @cite_10 @cite_13 designed more complicated frameworks to learn more robust representations with increasing computational cost or requiring extra information during inference.
@cite_35 @cite_41 @cite_36 @cite_42 @cite_10 @cite_18 @cite_39 @cite_23 @cite_37 @cite_7 @cite_28 @cite_40 @cite_16 @cite_12 @cite_9 @cite_0 @cite_24 @cite_45 @cite_47 @cite_13
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Person re-identification (reID) is an important task that requires to retrieve a person's images from an image dataset, given one image of the person of interest. For learning robust person features, the pose variation of person images is one of the key challenges. Existing works targeting the problem either perform human alignment, or learn human-region-based representations. Extra pose information and computational cost is generally required for inference. To solve this issue, a Feature Distilling Generative Adversarial Network (FD-GAN) is proposed for learning identity-related and pose-unrelated representations. It is a novel framework based on a Siamese structure with multiple novel discriminators on human poses and identities. In addition to the discriminators, a novel same-pose loss is also integrated, which requires appearance of a same person's generated images to be similar. After learning pose-unrelated person features with pose guidance, no auxiliary pose information and additional computational cost is required during testing. Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN. Abstract One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc ., and the high computational complexity. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent works. 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 Person re-identification (ReID) is a challenging problem, where global features of person images are not enough to solve unaligned image pairs. Many previous works used human pose information to acquire aligned local features to boost the performance. However, those methods need extra labeled data to train an available human pose estimation model. In this paper, we propose a novel method named Dynamically Matching Local Information (DMLI) that could dynamically align local information without requiring extra supervision. DMLI could achieve better performance, especially when encountering the human pose misalignment caused by inaccurate person detection boxes. Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI. AlignedReID++ improves the performance of global features, and could use DMLI to further increase accuracy in the inference phase. Experiments show effectiveness of our proposed method in comparison with several state-of-the-art person ReID approaches. Additionally, it achieves rank-1 accuracy of 92.8 on Market1501 and 86.2 on DukeMTMCReID with ResNet50. The code and models have been released 2 .