Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios

Li, Dangwei; Chen, Xiaotang; Huang, Kaiqi

doi:10.1109/acpr.2015.7486476

Cited by 236 publications

(160 citation statements)

References 13 publications

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“…Sudowe et al [28] propose a holistic CNN model to jointly learn different attributes. Li et al [13] formulate pedestrian attribute recognition as a multi-label classification problem and propose an improved cross-entropy loss function. However, the performance of these holistic methods is limited due to the lack of consideration of the prior information in attributes.…”

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)

119

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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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)

119

109

View full text Add to dashboard Cite

show abstract

“…After verifing the effect of the proposed method, we compared the proposed approach with several state-of-the-art approaches, e.g. DeepMAR [Li et al, 2015], HP-net [Liu et al, 2017b], SR C-RNN [Liu et al, 2017a], VAA [Sarafianos et al, 2018], andGRL [Zhao et al, 2018]. Results of comparison models are mostly reported from their papers directly.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Attribute Aware Pooling for Pedestrian Attribute Recognition

Han

Wang

Han

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition. * We thank anonymous reviewers for their helpful comments.

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“…To be specific, for N -way Kshot incremental few-shot learning, we first sample N attribute groups (see [Zhao et al, 2018]), then randomly choose one attribute from each of the selected N groups to form A novel , and treat the remaining attributes as A base . We follow [Li et al, 2015] and [Li et al, 2016], and divide the PETA and RAP into train/val/test set with 5 random partitions. All the reported results are the average results of these 5 partitions.…”

Section: Data Preparationmentioning

confidence: 99%

“…Base Network. Since we focus on the learning algorithm which is agnostic to the specific model, we adopt DeepMAR [Li et al, 2015] with ResNet50 as backbone as our base model M base for its competitive performances and simplicity. Hyperparameters.…”

Section: Implemetation Detailsmentioning

confidence: 99%

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Xiang

Jin

Ding

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a fixed set of attributes. They are unable to handle the incremental fewshot learning scenario, i.e. adapting a well-trained model to newly added attributes with scarce data, which commonly exists in the real world. In this work, we present a meta learning based method to address this issue. The core of our framework is a meta architecture capable of disentangling multiple attribute information and generalizing rapidly to new coming attributes. By conducting extensive experiments on the benchmark dataset PETA and RAP under the incremental few-shot setting, we show that our method is able to perform the task with competitive performances and low resource requirements.

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Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios

Cited by 236 publications

References 13 publications

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

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

Attribute Aware Pooling for Pedestrian Attribute Recognition

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

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