Deeply-Learned Part-Aligned Representations for Person Re-identification
Liming Zhao,Xi Li,Yueting Zhuang,Jingdong Wang +3 more
- pp 3239-3248
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TLDR
This paper proposes a simple yet effective human part-aligned representation for handling the body part misalignment problem, and shows state-of-the-art results over standard datasets, Market-1501,CUHK03, CUHK01 and VIPeR.Abstract:Â
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market-1501, CUHK03, CUHK01 and VIPeR. 1read more
Citations
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Book ChapterDOI
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
TL;DR: In this paper, a part-based convolutional baseline (PCB) is proposed to learn discriminative part-informed features for person retrieval and two contributions are made: (i) a network named Part-based Convolutional Baseline (PCBB) which outputs a convolutionAL descriptor consisting of several part-level features.
Posted Content
Random Erasing Data Augmentation
TL;DR: In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values and yields consistent improvement over strong baselines in image classification, object detection and person re-identification.
Proceedings ArticleDOI
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
TL;DR: Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that the proposed end-to-end feature learning strategy robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin.
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 Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
TL;DR: Zhang et al. as mentioned in this paper proposed a part-based convolutional baseline (PCB) for person retrieval, which employs part-level features to learn discriminative part-informed features for pedestrian image description.
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