Proceedings ArticleDOI
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification
Faqiang Wang,Wangmeng Zuo,Liang Lin,David Zhang,Lei Zhang +4 more
- pp 1288-1296
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TLDR
This work proposes a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN), and finds that the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance.Abstract:
Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.read more
Citations
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Posted Content
In Defense of the Triplet Loss for Person Re-Identification
TL;DR: It is shown that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.
Proceedings ArticleDOI
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro
TL;DR: A simple semisupervised pipeline that only uses the original training set without collecting extra data, which effectively improves the discriminative ability of learned CNN embeddings and proposes the label smoothing regularization for outliers (LSRO).
Proceedings ArticleDOI
Harmonious Attention Network for Person Re-identification
Wei Li,Xiatian Zhu,Shaogang Gong +2 more
TL;DR: A novel Harmonious Attention CNN (HA-CNN) model is formulated for joint learning of soft pixel attention and hard regional attention along with simultaneous optimisation of feature representations, dedicated to optimise person re-id in uncontrolled (misaligned) images.
Posted Content
Beyond triplet loss: a deep quadruplet network for person re-identification
TL;DR: A quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID, which can lead to the model output with a larger inter- class variation and a smaller intra-class variation compared to the triplet loss.
Proceedings ArticleDOI
Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification
TL;DR: In this article, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for person ReID, which can lead to the model output with a larger interclass variation and a smaller intra-class variation compared to the triplet loss.
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