Divide and Fuse: A Re-ranking Approach for Person Re-identification

Yu, Rui; Zhou, Zhichao; Bai, Song; Bai, Xiang

doi:10.5244/c.31.135

Cited by 58 publications

(32 citation statements)

References 30 publications

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“…Finally we report the performance of our approach with other stateof-the-art results in Tables 4 and 5. Note that we do not apply any post processing such as re-ranking [51] or multi-query fusion [53]. On each dataset, our approach attains the best performance.…”

Section: Discriminative Evaluationsmentioning

confidence: 99%

Joint Discriminative and Generative Learning for Person Re-Identification

Zheng

Yang

Yu³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

686

324

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Person re-identification (re-id) remains challenging due to significant intra-class variations across different cameras. Recently, there has been a growing interest in using generative models to augment training data and enhance the invariance to input changes. The generative pipelines in existing methods, however, stay relatively separate from the discriminative re-id learning stages. Accordingly, re-id models are often trained in a straightforward manner on the generated data. In this paper, we seek to improve learned re-id embeddings by better leveraging the generated data. To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end. Our model involves a generative module that separately encodes each person into an appearance code and a structure code, and a discriminative module that shares the appearance encoder with the generative module. By switching the appearance or structure codes, the generative module is able to generate high-quality cross-id composed images, which are online fed back to the appearance encoder and used to improve the discriminative module. The proposed joint learning framework renders significant improvement over the baseline without using generated data, leading to the stateof-the-art performance on several benchmark datasets.

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Section: Discriminative Evaluationsmentioning

confidence: 99%

Joint Discriminative and Generative Learning for Person Re-Identification

Zheng

Yang

Yu³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

686

324

View full text Add to dashboard Cite

show abstract

“…Along this line, Sarfraz et al [30] aggregated distances between expanded neighbors of image pairs to reinforce the original pairwise distance. Moreover, to take advantage of the diversity within a single feature, Yu et al [56] further fused distances between different sub-features. Spectral clustering.…”

Section: Related Workmentioning

confidence: 99%

Spectral Feature Transformation for Person Re-Identification

Luo

Chen

Wang³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

113

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With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years. Deep learning based methods focus on learning a feature space where samples are clustered compactly according to their corresponding identities. Most existing methods rely on powerful CNNs to transform the samples individually. In contrast, we propose to consider the sample relations in the transformation. To achieve this goal, we incorporate spectral clustering technique into CNN. We derive a novel module named Spectral Feature Transformation and seamlessly integrate it into existing CNN pipeline with negligible cost, which makes our method enjoy the best of two worlds. Empirical studies show that the proposed approach outperforms previous state-of-the-art methods on four public benchmarks by a considerable margin without bells and whistles.

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“…In Table 6, we compare with the current best models. A total of 11 representative state-of-the-art methods, BOW+XQDA [53], PUL [7], LOMO+XQDA [25], IDE [54], IDE+DaF [51], IDE+XQ.+Re-ranking [55], PAN, DPFL [4], and the newly proposed methods SVDNet [39], TriNet + Era. [56], and TriNet + Era.…”

Section: Cuhk03 Person Re-identificationmentioning

confidence: 99%

Adaptively Connected Neural Networks

Wang¹,

Wang

Li³

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) in two aspects. First, ACNet employs a flexible way to switch global and local inference in processing the internal feature representations by adaptively determining the connection status among the feature nodes (e.g., pixels of the feature maps) 1 . We can show that existing CNNs, the classical multilayer perceptron (MLP), and the recently proposed non-local network (NLN) [48] are all special cases of ACNet. Second, ACNet is also capable of handling non-Euclidean data. Extensive experimental analyses on a variety of benchmarks (i.e., ImageNet-1k classification, COCO 2017 detection and segmentation, CUHK03 person re-identification, CIFAR analysis, and Cora document categorization) demonstrate that ACNet cannot only achieve state-of-the-art performance but also overcome the limitation of the conventional MLP and CNN 2 . The code is available at https://github.com/wanggrun/ Adaptively-Connected-Neural-Networks.

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Divide and Fuse: A Re-ranking Approach for Person Re-identification

Cited by 58 publications

References 30 publications

Joint Discriminative and Generative Learning for Person Re-Identification

Joint Discriminative and Generative Learning for Person Re-Identification

Spectral Feature Transformation for Person Re-Identification

Adaptively Connected Neural Networks

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