AttriMeter: An Attribute-guided Metric Interpreter for Person Re-Identification

Person Re-identification (ReID) has achieved significant improvement due to the adoption of Convolutional Neural Networks (CNNs). However, person ReID systems only provide a distance or similarity when matching two persons, which makes users hardly understand why they are similar or not. Therefore, we propose an Attribute-guided Metric Interpreter, named AttriMeter, to semantically and quantitatively explain the results of CNN-based ReID models. The AttriMeter has a pluggable structure that can be grafted on arbitrary target models, i.e., the ReID models that need to be interpreted. With an attribute decomposition head, it can learn to generate a group of attribute-guided attention maps (AAMs) from the target model. By applying AAMs to features of two persons from the target model, their distance will be decomposed into a set of attribute-guided components which can measure the contributions of individual attributes. Moreover, we design a distance distillation loss to guarantee the consistency between the results from the target model and the decomposed components from AttriMeter, and an attribute prior loss to eliminate the biases caused by the unbalanced distribution of attributes. Finally, extensive experiments and analysis on a variety of ReID models and datasets show the effectiveness of AttriMeter.

[1]  Xiaoou Tang,et al.  Pedestrian Attribute Recognition At Far Distance , 2014, ACM Multimedia.

[2]  Kaiqi Huang,et al.  Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[4]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

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

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

[7]  Christian Poellabauer,et al.  Second-Order Non-Local Attention Networks for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[8]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Andrea Vedaldi,et al.  Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Lucas Beyer,et al.  In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.

[11]  Xixian Chen,et al.  Towards Global Explanations of Convolutional Neural Networks With Concept Attribution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Yichen Wei,et al.  Circle Loss: A Unified Perspective of Pair Similarity Optimization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Bolei Zhou,et al.  Understanding the role of individual units in a deep neural network , 2020, Proceedings of the National Academy of Sciences.

[14]  Jingdong Wang,et al.  Deeply-Learned Part-Aligned Representations for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Quanshi Zhang,et al.  Explaining Neural Networks Semantically and Quantitatively , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[16]  Shaogang Gong,et al.  Harmonious Attention Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Bolei Zhou,et al.  Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Kaiqi Huang,et al.  A Richly Annotated Dataset for Pedestrian Attribute Recognition , 2016, ArXiv.

[19]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[20]  Qi Tian,et al.  Beyond Part Models: Person Retrieval with Refined Part Pooling , 2017, ECCV.

[21]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[23]  Tao Mei,et al.  FastReID: A Pytorch Toolbox for General Instance Re-identification , 2020, ArXiv.

[24]  Cuiling Lan,et al.  Relation-Aware Global Attention for Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[27]  Xiong Chen,et al.  Learning Discriminative Features with Multiple Granularities for Person Re-Identification , 2018, ACM Multimedia.

[28]  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).

[29]  Lalana Kagal,et al.  Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

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

[31]  Quanshi Zhang,et al.  Interpreting CNNs via Decision Trees , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).