Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification

Li, Dangwei; Chen, Xiaotang; Zhang, Zhang; Huang, Kaiqi

doi:10.1109/cvpr.2017.782

Cited by 635 publications

(371 citation statements)

References 54 publications

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“…Person re-identification. Person reID methods focus on two key points: learning a powerful feature representation for images [8,43,14,44,24,26,28,42,1] and designing an effective distance metric [32,29,56,2,57]. Recently, deep learning approaches have obtained state-of-art results for reID.…”

Section: Related Workmentioning

confidence: 99%

Interaction-And-Aggregation Network for Person Re-Identification

Hou

Chang

et al. 2019

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

341

141

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Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for smallscale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets. ID Conv Block Conv Block …. Conv Block Conv Block …. Conv Block Conv Block …. Conv Block Conv Block

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Section: Related Workmentioning

confidence: 99%

Interaction-And-Aggregation Network for Person Re-Identification

Hou

Chang

et al. 2019

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

341

141

View full text Add to dashboard Cite

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“…... 32 × 16 ... 16 × 8 ... Some recent works [14,17] adopt STN to localize bodyparts for person re-identification. Fu et al [3] attempt to recursively learn discriminative region for fine-grained image recognition.…”

Section: Related Workmentioning

confidence: 99%

Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

Tang

Sheng

Zhang

et al. 2019

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

111

109

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Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K.

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“…Attention serves as a tool to bias the allocation of available resources towards the most informative parts of an input. Li et al [24] propose a part-aligning CNN network for locating latent regions (i.e. hard attention) and then extract and exploit these regional features for ReID.…”

Section: Related Workmentioning

confidence: 99%

Mixed High-Order Attention Network for Person Re-Identification

Chen¹,

Deng

2019

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

360

188

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Attention has become more attractive in person reidentification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an input signal. However, state-of-the-art works concentrate only on coarse or first-order attention design, e.g. spatial and channels attention, while rarely exploring higher-order attention mechanism. We take a step towards addressing this problem. In this paper, we first propose the High-Order Attention (HOA) module to model and utilize the complex and high-order statistics information in attention mechanism, so as to capture the subtle differences among pedestrians and to produce the discriminative attention proposals. Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner. Extensive experiments have been conducted to validate the superiority of our MHN for person ReID over a wide variety of state-of-the-art methods on three large-scale datasets, including Market-1501, DukeMTMC-ReID and CUHK03-NP. Code is available at

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Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification

Cited by 635 publications

References 54 publications

Interaction-And-Aggregation Network for Person Re-Identification

Interaction-And-Aggregation Network for Person Re-Identification

Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

Mixed High-Order Attention Network for Person Re-Identification

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