Identity-sensitive loss guided and instance feature boosted deep embedding for person search

Neurocomputing - Tập 415 - Trang 1-14 - 2020
Wei Shi1, Hong Liu2, Mengyuan Liu2,3
1Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing 100871, China
2Tencent Research, Singapore
3School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

Tài liệu tham khảo

Han, 2019, Re-id driven localization refinement for person search, 9814 Lan, 2018, Person search by multi-scale matching, 536 H. Liu, W. Shi, W. Huang, Q. Guan, A discriminatively learned feature embedding based on multi-loss fusion for person search, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, pp. 1668–1672. Shi, 2018, Instance enhancing loss: feep identity-sensitive feature embedding for person search, 4108 Borgia, 2018, Cross-view discriminative feature learning for person re-identification, IEEE Trans. Image Process., 27, 5338, 10.1109/TIP.2018.2851098 Chen, 2015, Similarity learning on an explicit polynomial kernel feature map for person re-identification, 1565 Chen, 2017, Beyond triplet loss: a deep quadruplet network for person re-identification Ding, 2015, Deep feature learning with relative distance comparison for person re-identification, Pattern Recogn., 48, 2993, 10.1016/j.patcog.2015.04.005 Farenzena, 2010, Person re-identification by symmetry-driven accumulation of local features, 2360 Felzenszwalb, 2009, Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell., 32, 1627, 10.1109/TPAMI.2009.167 Gao, 2019, Structure-aware person search with self-attention and online instance aggregation matching, Neurocomputing, 369, 29, 10.1016/j.neucom.2019.08.038 Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation, 580 He, 2016, Deep residual learning for image recognition, 770 Hinton, 2014, Distilling the knowledge in a neural network Huang, 2017, DeepDiff: Learning deep difference features on human body parts for person re-identification, Neurocomputing, 241, 191, 10.1016/j.neucom.2017.02.055 Jia, 2014, Caffe: convolutional architecture for fast feature embedding, 675 Koestinger, 2012, Large scale metric learning from equivalence constraints, 2288 Letsch, 2019, Localizing salient body motion in multi-person scenes using convolutional neural networks, Neurocomputing, 95, 449, 10.1016/j.neucom.2018.11.048 Li, 2018, Unsupervised person re-identification by deep learning tracklet association, 737 Li, 2014, Deepreid: Deep filtering pairing neural network for person re-identification, 152 Liao, 2015, Person re-identification by local maximal occurrence representation and metric learning, 2197 Lin, 2019, A bottom-up clustering approach to unsupervised person re-identification, 8738 Liu, 2017, Neural person search machine, 493 Liu, 2017, End-to-end comparative attention networks for person re-identification, IEEE Trans. Image Process., 26, 3492, 10.1109/TIP.2017.2700762 H. Liu, W. Huang, Body structure based triplet convolutional neural network for person re-identification, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2017, pp. 1772–1776. Liu, 2016, Depth Context: a new descriptor for human activity recognition by using sole depth sequences, Neurocomputing, 175, 747, 10.1016/j.neucom.2015.11.005 Liu, 2017, Enhanced skeleton visualization for view invariant human action recognition, Pattern Recogn., 68, 346, 10.1016/j.patcog.2017.02.030 Liu, 2014, Fashion parsing with weak color-category label, IEEE Trans. Multimedia, 16, 253, 10.1109/TMM.2013.2285526 Liu, 2016, SSD: Single shot multibox detector, 21 Ma, 2012, Local descriptors encoded by fisher vectors for person re-identification, 413 B.J. Prosser, W.S. Zheng, S. Gong, T. Xiang, Q. Mary, Person re-identification by support vector ranking, in: proceedings of the British Machine Vision Conference 2, 2010, pp. 1–6. Ren, 2017, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031 Schumann, 2017, Deep learning prototype domains for person re-identification, 1767 Stauffer, 1999, Adaptive background mixture models for real-time tracking, 246 Sun, 2018, Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline), 480 Sutskever, 2013, On the importance of initialization and momentum in deep learning, 1139 Varior, 2016, Gated siamese convolutional neural network architecture for human re-identification, 791 Wang, 2013, Intelligent multi-camera video surveillance: a review, Pattern Recogn. Lett., 34, 3, 10.1016/j.patrec.2012.07.005 Wen, 2016, A discriminative feature learning approach for deep face recognition, 499 Wu, 2019, Deep learning-based methods for person re-identification: a comprehensive review, Neurocomputing, 337, 354, 10.1016/j.neucom.2019.01.079 Wu, 2016, An enhanced deep feature representation for person re-identification, 1 Xiao, 2019, IAN: the individual aggregation network for person search, Pattern Recogn., 87, 332, 10.1016/j.patcog.2018.10.028 Xiao, 2016, Learning deep feature representations with domain guided dropout for person re-identification, 1249 T. Xiao, S. Li, B. Wang, L. Lin, X. Wang, . End-to-end deep learning for person search, 2016. arXiv preprint arXiv:1604.01850. Xiao, 2017, Joint detection and identification feature learning for person search, 3376 Xu, 2014, Person search in a scene by jointly modeling people commonness and person uniqueness, 937 Yang, 2015, Convolutional channel features, 82 Yi, 2014, Deep metric learning for person re-identification, 34 Z. Zhong, L. Zheng, Z.L.S.L., Y. Yang, Invariance matters: Exemplar memory for domain adaptive person re-identification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 598–607. Zhao, 2013, Unsupervised salience learning for person re-identification, 3586 Zhao, 2014, Learning mid-level filters for person re-identification, 144 Zheng, 2015, Scalable person re-identification: a benchmark, 1116 Zheng, 2017, Person re-identification in the wild, 1367 Zheng, 2017, Unlabeled samples generated by gan improve the person re-identification baseline in vitro, 3754