Multiscale Omnibearing Attention Networks for Person Re-Identification

Huang, Yewen; Lian, Sicheng; Hu, Haifeng; Chen, Dihu; Su, Tao

doi:10.1109/tcsvt.2020.3014167

Cited by 24 publications

(4 citation statements)

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“…An end-to-end approach [171] to extract holistc and local feature maps using multi-scale omnibearing attention network. Multi-sized convolutions were used to obtain the local and holistic feature maps.…”

Section: Attention-based Approachesmentioning

confidence: 99%

“…The scale differences are handled by various re-id solutions, however, the attention based approaches outperformed the rest of re-id solutions. The top three solutions either used the multi-scale attention pyramid [165] or divided the image into multiple local parts and then learnt the attention [169], or [171] extracted the holistc and local feature maps using multi-scale omni-bearing attention network. The re-id solutions that support multi-scale re-id learn the person features at multiple scales through multi-sized convolutional layers or branches.…”

Section: Deep Learning Conjecturementioning

confidence: 99%

See 1 more Smart Citation

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

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Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale & viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions. Scope/Objective of the ReviewIn this paper, we have targeted the most popular challenges in person re-id to perform systematic challenge-wise review of the published approaches. In this context, we have collected papers from top conferences and journals for the years from 2015 to 2021. The progress in papers addressing each challenge and its influence on published results is

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Section: Attention-based Approachesmentioning

confidence: 99%

Section: Deep Learning Conjecturementioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Wu et al [29] extract the camera information and then attend to them. Huang et al [30] proposes a method to spatially pay attention to the region of interest, and the discriminative information of the image will be magnified. Also, Huang et al [25] propose 3-Dimension Transmissible Attention (3DTA) that cooperatively utilizes channel attention and spatial attention, with a group loss to optimize the feature distances.…”

Section: Related Work a Supervised Person Re-identificationmentioning

confidence: 99%

Noise Resistible Network for Unsupervised Domain Adaptation on Person Re-Identification

Zhang

Zeng

et al. 2021

IEEE Access

Self Cite

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Unsupervised domain adaptation on person re-identification (re-ID), which adapts the model trained on source dataset to the target dataset, has drawn increasing attention over the past few years. It is more practical than the traditional supervised methods when applied in the real-world scenarios since they require a huge number of manual annotations in a specific domain, which is unrealistic and even under personal privacy concerns. Currently, pseudo label-based method is one of the most promising solutions in this area. However, in such methods, pseudo label noise is ignored and remains a huge challenge hindering further performance improvements. To solve this problem, this paper proposes a novel unsupervised domain adaptation re-ID framework named Noise Resistible Network (NRNet), which mainly consists of two dual-stream networks. For one thing, during pseudo label generation, NRNet utilizes one dual-stream network, denoted as clustering network, to generate discriminative features in the unseen domain for further clustering, reducing the pseudo label noise. For another, to avoid the problem of close loop noise amplification in conventional methods, the other dual-stream network named temporally average network is constructed outside the clustering loop to learn how to identify the images of the same person. In addition, two dual-stream networks are designed with a guiding mechanism, which allows the shallow network to learn more representative feature embedding from the deep network. Extensive experimental results on two widely-used benchmark datasets, i.e., Market-1501 and DukeMTMC-reID demonstrate that our proposed NRNet outperforms the state-of-the-art methods.INDEX TERMS Unsupervised domain adaptation, person re-identification, noise resistible network.

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“…Both phases are complex and very interesting. For feature extraction, modern approaches have leveraged deep learning techniques, exploiting convolutional networks [ 4 , 5 ], Generative Adversarial Networks [ 6 , 7 , 8 , 9 ], Visual Transformers [ 10 , 11 , 12 , 13 , 14 ], and different kinds of attention mechanisms [ 15 , 16 , 17 , 18 ]. Features can be enhanced by exploiting part-based methods, focusing on extracting features from specific regions of interest [ 10 , 19 , 20 ], pretraining [ 21 , 22 ], and multitasking [ 23 ] via a suitable set of pretext tasks.…”

Section: Introductionmentioning

confidence: 99%

A Generative Approach to Person Reidentification

Asperti,

Fiorilla,

Orsini

2024

Sensors

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Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.

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Multiscale Omnibearing Attention Networks for Person Re-Identification

Cited by 24 publications

References 50 publications

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Noise Resistible Network for Unsupervised Domain Adaptation on Person Re-Identification

A Generative Approach to Person Reidentification

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