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Camera Style Adaptation for Person Re-identification
Zhun Zhong, Liang Zheng, Zhedong Zheng +2
Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning…
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
Yifan Sun, Liang Zheng, Yi Yang +2
Employing part-level features for pedestrian image description offers fine-grained information and has been verified as beneficial for person retrieval in very recent literature. A…
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
Zhedong Zheng, Liang Zheng, Michael Garrett +3
Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visua…
Random Erasing Data Augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang +2
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a recta…
Pedestrian Alignment Network for Large-scale Person Re-identification
Zhedong Zheng, Liang Zheng, Yi Yang
Person re-identification (person re-ID) is mostly viewed as an image retrieval problem. This task aims to search a query person in a large image pool. In practice, person re-ID usu…
Unsupervised Person Re-identification: Clustering and Fine-tuning
Hehe Fan, Liang Zheng, Yi Yang
The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this paper, we consider the more pr…