In Defense of the Triplet Loss for Person Re-Identification

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.

[1]  Kaiqi Huang,et al.  A Multi-Task Deep Network for Person Re-Identification , 2016, AAAI.

[2]  Shaogang Gong,et al.  Person Re-identification by Deep Learning Multi-scale Representations , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[3]  Rui Yu,et al.  Divide and Fuse: A Re-ranking Approach for Person Re-identification , 2017, BMVC.

[4]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[5]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[6]  Dong Liu,et al.  Multi-Scale Triplet CNN for Person Re-Identification , 2016, ACM Multimedia.

[7]  Nanning Zheng,et al.  Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Laurens van der Maaten,et al.  Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..

[9]  Yi Yang,et al.  A Discriminatively Learned CNN Embedding for Person Reidentification , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[10]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[11]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Larry S. Davis,et al.  Joint Learning for Attribute-Consistent Person Re-Identification , 2014, ECCV Workshops.

[13]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[14]  Shengcai Liao,et al.  Embedding Deep Metric for Person Re-identification: A Study Against Large Variations , 2016, ECCV.

[15]  Barbara Caputo,et al.  Looking beyond appearances: Synthetic training data for deep CNNs in re-identification , 2017, Comput. Vis. Image Underst..

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Razvan Pascanu,et al.  Theano: new features and speed improvements , 2012, ArXiv.

[19]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Qi Tian,et al.  Scalable Person Re-identification on Supervised Smoothed Manifold , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Shiliang Zhang,et al.  Deep Attributes Driven Multi-Camera Person Re-identification , 2016, ECCV.

[23]  Shaogang Gong,et al.  Person Re-Identification by Deep Joint Learning of Multi-Loss Classification , 2017, IJCAI.

[24]  Liang Zheng,et al.  Improving Person Re-identification by Attribute and Identity Learning , 2017, Pattern Recognit..

[25]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  David Zhang,et al.  Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Lucas Beyer,et al.  The STRANDS Project: Long-Term Autonomy in Everyday Environments , 2016, IEEE Robotics Autom. Mag..

[28]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

[29]  Shaogang Gong,et al.  Learning a Discriminative Null Space for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Rainer Stiefelhagen,et al.  Person Re-identification by Deep Learning Attribute-Complementary Information , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[31]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[32]  Gang Wang,et al.  Gated Siamese Convolutional Neural Network Architecture for Human Re-identification , 2016, ECCV.

[33]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[34]  Xiaogang Wang,et al.  Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Tao Xiang,et al.  Deep Transfer Learning for Person Re-Identification , 2016, 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM).

[37]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Yifan Sun,et al.  SVDNet for Pedestrian Retrieval , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[39]  Silvio Savarese,et al.  Deep Metric Learning via Lifted Structured Feature Embedding , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[41]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[42]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[43]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[44]  Liang Lin,et al.  Deep feature learning with relative distance comparison for person re-identification , 2015, Pattern Recognit..

[45]  Anton van den Hengel,et al.  Learning to rank in person re-identification with metric ensembles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Kaiqi Huang,et al.  Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Daniel Cremers,et al.  SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports , 2015, FSR.

[49]  Kan Liu,et al.  Learning Compact Appearance Representation for Video-Based Person Re-Identification , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[50]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[51]  Yi Yang,et al.  Pedestrian Alignment Network for Large-scale Person Re-Identification , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[52]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Anurag Mittal,et al.  Deep Neural Networks with Inexact Matching for Person Re-Identification , 2016, NIPS.

[54]  Huchuan Lu,et al.  Deep Mutual Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[55]  Yi Yang,et al.  Person Re-identification: Past, Present and Future , 2016, ArXiv.