{"type":"ScholarlyArticle","title":"Attribute Aware Pooling for Pedestrian Attribute Recognition","authors":[{"name":"Kai Han","slug":"kai-han","given":"Kai","family":"Han","affiliation":"s Ark Lab"},{"name":"Yunhe Wang","slug":"yunhe-wang","given":"Yunhe","family":"Wang","affiliation":"s Ark Lab"},{"name":"Han Shu","slug":"han-shu","given":"Han","family":"Shu","affiliation":"s Ark Lab"},{"name":"Chuanjian Liu","slug":"chuanjian-liu","given":"Chuanjian","family":"Liu","affiliation":"s Ark Lab"},{"name":"Chunjing Xu","slug":"chunjing-xu","given":"Chunjing","family":"Xu","affiliation":"s Ark Lab"},{"name":"Chang Xu","slug":"chang-xu","given":"Chang","family":"Xu","affiliation":"s Ark Lab"}],"abstract":"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.","doi":"10.24963/ijcai.2019/341","url":"https://ijcai.org/proceedings/2019/341","pdf":"https://ijcai.org/proceedings/2019/0341.pdf","markdown":"https://ijcai.org/proceedings/2019/341.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/341","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":341,"pages":{"first":2456,"last":2462},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Deep Learning","Computer Vision: Computer Vision","Computer Vision: Biometrics, Face and Gesture Recognition"],"videos":[],"references":[{"position":1,"raw":"[Boguraev and Ando, 2005] Branimir Boguraev and Rie Kubota Ando. Timeml-compliant text analysis for temporal reasoning. In IJCAI, 2005.","title":"Timeml-compliant text analysis for temporal reasoning","authors":"Ando Boguraev, Branimir Boguraev, Rie Kubota, Ando","year":"2005","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Deng et al., 2014] Yubin Deng, Ping Luo, Chen Change Loy, and Xiaoou Tang. Pedestrian attribute recognition at far distance. In ACM MM, 2014.","title":"Pedestrian attribute recognition at far distance","authors":"Yubin Deng, Ping Luo, Chen Change Loy, Xiaoou Tang","year":"2014","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Guo and Gu, 2011] Yuhong Guo and Suicheng Gu. Multi-label classification using conditional dependency networks. In IJCAI, 2011.","title":"Multi-label classification using conditional dependency networks","authors":"Gu ; ] Yuhong Guo, Suicheng Guo, Gu","year":"2011","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Han et al., 2018a] Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. Attribute-aware attention model for fine-grained representation learning. In ACM MM, 2018.","title":"Attribute-aware attention model for fine-grained representation learning","authors":"Han","year":"2018","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Han et al., 2018b] Kai Han, Yunhe Wang, Chao Zhang, Chao Li, and Chao Xu. Autoencoder inspired unsupervised feature selec- tion. In ICASSP. IEEE, 2018.","title":"Autoencoder inspired unsupervised feature selection","authors":"Han","year":"2018","venue":"ICASSP. IEEE","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hand and Chellappa, 2017] Emily M Hand and Rama Chellappa. Attributes for improved attributes: A multi-task network utiliz- ing implicit and explicit relationships for facial attribute classifi- cation. In AAAI, pages 4068-4074, 2017.","title":"Attributes for improved attributes: A multi-task network utilizing implicit and explicit relationships for facial attribute classification","authors":"M Emily, Rama Hand, Chellappa","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Layne et al., 2012] Ryan Layne, Timothy M Hospedales, Shao- gang Gong, and Q Mary. Person re-identification by attributes. In Bmvc, volume 2, page 8, 2012.","title":"Person re-identification by attributes","authors":"Ryan Layne, Timothy M Hospedales, Shaogang Gong, Mary","year":"2012","venue":"Bmvc","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2015] Dangwei Li, Xiaotang Chen, and Kaiqi Huang. Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In ACPR, 2015.","title":"Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios","authors":"Li","year":"2015","venue":"ACPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2016] Dangwei Li, Zhang Zhang, Xiaotang Chen, Haibin Ling, and Kaiqi Huang. A richly annotated dataset for pedestrian attribute recognition. arXiv preprint arXiv:1603.07054, 2016.","title":"A richly annotated dataset for pedestrian attribute recognition","authors":"Dangwei Li, Zhang Zhang, Xiaotang Chen, Haibin Ling, Kaiqi Huang","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2017] Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, and Anton van den Hengel. Sequential per- son recognition in photo albums with a recurrent network. In CVPR, 2017.","title":"Sequential person recognition in photo albums with a recurrent network","authors":"Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Anton Van Den, Hengel","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2017a] Feng Liu, Tao Xiang, Timothy M. Hospedales, Wankou Yang, and Changyin Sun. Semantic regularisation for recurrent image annotation. In CVPR, July 2017.","title":"Semantic regularisation for recurrent image annotation","authors":"Liu","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2017b] Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang. Hydraplus-net: Attentive deep features for pedestrian analysis. In ICCV, 2017.","title":"Hydraplus-net: Attentive deep features for pedestrian analysis","authors":"Liu","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2018] Pengze Liu, Xihui Liu, Junjie Yan, and Jing Shao. Localization guided learning for pedestrian attribute recognition. In BMVC, 2018.","title":"Localization guided learning for pedestrian attribute recognition","authors":"Liu","year":"2018","venue":"BMVC","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lu et al., 2017] Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris. Fully-adaptive fea- ture sharing in multi-task networks with applications in person attribute classification. In CVPR, volume 1, page 6, 2017.","title":"Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification","authors":"Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogerio Feris","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Luo et al., 2018] Yong Luo, Yonggang Wen, and Dacheng Tao. Heterogeneous multitask metric learning across multiple do- mains. IEEE T-NNLS, 29(9):4051-4064, 2018.","title":"Heterogeneous multitask metric learning across multiple domains","authors":"Yong Luo, Yonggang Wen, Dacheng Tao","year":"2018","venue":"IEEE T-NNLS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Luo et al., 2019] Yong Luo, Yonggang Wen, Tongliang Liu, and Dacheng Tao. Transferring knowledge fragments for learning distance metric from a heterogeneous domain. IEEE T-PAMI, 41(4):1013-1026, 2019.","title":"Transferring knowledge fragments for learning distance metric from a heterogeneous domain","authors":"Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao","year":"2019","venue":"IEEE T-PAMI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mensink et al., 2014] Thomas Mensink, Efstratios Gavves, and Cees GM Snoek. Costa: Co-occurrence statistics for zero-shot classification. In CVPR, 2014.","title":"Costa: Co-occurrence statistics for zero-shot classification","authors":"Thomas Mensink, Efstratios Gavves, G M Cees, Snoek","year":"2014","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Modiri Assari et al., 2014] Shayan Modiri Assari, Amir Roshan Zamir, and Mubarak Shah. Video classification using semantic concept co-occurrences. In CVPR, 2014.","title":"Video classification using semantic concept co-occurrences","authors":"Shayan Modiri, Assari, Amir Roshan Zamir, Mubarak Shah","year":"2014","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Paszke et al., 2017] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. In NIPS-W, 2017.","title":"Automatic differentiation in pytorch","authors":"Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary Devito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer","year":"2017","venue":"NIPS-W","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Peng et al., 2016] Peixi Peng, Yonghong Tian, Tao Xiang, Yaowei Wang, and Tiejun Huang. Joint learning of semantic and latent attributes. In ECCV, 2016.","title":"Joint learning of semantic and latent attributes","authors":"Peng","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NeurIPS, 2015.","title":"Faster R-CNN: Towards real-time object detection with region proposal networks","authors":"Ren","year":"2015","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Ima- genet large scale visual recognition challenge. IJCV, 115(3):211- 252, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein","year":"2015","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sarafianos et al., 2018] Nikolaos Sarafianos, Xiang Xu, and Ioan- nis A Kakadiaris. Deep imbalanced attribute classification using visual attention aggregation. In ECCV, 2018.","title":"Deep imbalanced attribute classification using visual attention aggregation","authors":"Nikolaos Sarafianos, Xiang Xu, Ioannis A Kakadiaris","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sudowe et al., 2015] Patrick Sudowe, Hannah Spitzer, and Bastian Leibe. Person attribute recognition with a jointly-trained holistic cnn model. In ICCV Workshops, 2015.","title":"Person attribute recognition with a jointly-trained holistic cnn model","authors":"Patrick Sudowe, Hannah Spitzer, Bastian Leibe","year":"2015","venue":"ICCV Workshops","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sun et al., 2018] Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In ECCV, 2018.","title":"Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)","authors":"Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, Shengjin Wang","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2013] Yunhe Wang, Miaojing Shi, Yuan Gao, and Chao Xu. Visual words refining exploiting spatial co-occurrence table. In 2013 IEEE Global High Tech Congress on Electronics, pages 99-104. IEEE, 2013.","title":"Visual words refining exploiting spatial co-occurrence table","authors":"Yunhe Wang, Miaojing Shi, Yuan Gao, Chao Xu","year":"2013","venue":"IEEE Global High Tech Congress on Electronics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2016] Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, and Chao Xu. Cnnpack: Packing convolutional neural net- works in the frequency domain. In NeurIPS, 2016.","title":"Cnnpack: Packing convolutional neural networks in the frequency domain","authors":"Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu","year":"2016","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2017] Jingya Wang, Xiatian Zhu, Shaogang Gong, and Wei Li. Attribute recognition by joint recurrent learning of context and correlation. In ICCV, Oct 2017.","title":"Attribute recognition by joint recurrent learning of context and correlation","authors":"Wang","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2018] Yunhe Wang, Chang Xu, XU Chunjing, Chao Xu, and Dacheng Tao. Learning versatile filters for efficient con- volutional neural networks. In NeurIPS, 2018.","title":"Learning versatile filters for efficient convolutional neural networks","authors":"Yunhe Wang, Chang Xu, Chao Xu Chunjing, Dacheng Xu, Tao","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xiao et al., 2016] Tong Xiao, Hongsheng Li, Wanli Ouyang, and Xiaogang Wang. Learning deep feature representations with do- main guided dropout for person re-identification. In CVPR, 2016.","title":"Learning deep feature representations with domain guided dropout for person re-identification","authors":"Tong Xiao, Hongsheng Li, Wanli Ouyang, Xiaogang Wang","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yi et al., 2014] Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Deep metric learning for person re-identification. In ICPR, 2014.","title":"Deep metric learning for person re-identification","authors":"Dong Yi, Zhen Lei, Shengcai Liao, Stan Z Li","year":"2014","venue":"ICPR","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhao et al., 2018] Xin Zhao, Liufang Sang, Guiguang Ding, Yuchen Guo, and Xiaoming Jin. Grouping attribute recognition for pedestrian with joint recurrent learning. In IJCAI, 2018.","title":"Grouping attribute recognition for pedestrian with joint recurrent learning","authors":"Xin Zhao, Liufang Sang, Guiguang Ding, Yuchen Guo, Xiaoming Jin","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/441"},{"position":35,"raw":"[Zhu et al., 2015] Jianqing Zhu, Shengcai Liao, Dong Yi, Zhen Lei, and Stan Z Li. Multi-label cnn based pedestrian attribute learning for soft biometrics. In ICB. IEEE, 2015.","title":"Multi-label cnn based pedestrian attribute learning for soft biometrics","authors":"Jianqing Zhu, Shengcai Liao, Dong Yi, Zhen Lei, Stan Z Li","year":"2015","venue":"ICB. IEEE","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}