Multi-Branch Person Re-Identification Based on Multi-Scale Attention

Cong Li; Min Jiang; Jun Kong

doi:10.3788/LOP57.201001

[1] Liu K W, Fang P P, Xiong H X et al. Person re-identification based on multi-layer feature[J]. Laser & Optoelectronics Progress, 57, 081503(2020).

[2] Bi X J, Wang H. Person re-identification based on view information embedding[J]. Acta Optica Sinica, 39, 0615007(2019).

[3] Chen B, Zha Y F, Li Y Q et al. Person re-identification based on convolutional neural network discriminative feature learning[J]. Acta Optica Sinica, 38, 0720001(2018).

[4] Dai Z Z, Chen M Q, Gu X D et al. Batch DropBlock network for person re-identification and beyond[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 3691-3701(2019).

[5] Sun Y F, Zheng L, Yang Y et al[M]. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline), 501-518(2018).

[6] Wang G S, Yuan Y F, Chen X et al. Learning discriminative features with multiple granularities for person Re-identification[C]∥2018 ACM Multimedia Conference on Multimedia Conference - MM '18., 274-282(2018).

[7] Xu L Z, Peng L. Person reidentification based on multiscale convolutional feature fusion[J]. Laser & Optoelectronics Progress, 56, 141504(2019).

[8] Ghiasi G, Lin T Y, Le Q V. DropBlock: a regularization method for convolutional networks. [C]∥Advances in Neural Information Processing Systems., 10727-10737(2018).

[9] Xu J, Zhao R, Zhu F et al. Attention-aware compositional network for person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2119-2128(2018).

[10] Si J L, Zhang H G, Li C G et al. Dual attention matching network for context-aware feature sequence based person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake Ci, 5363-5372(2018).

[11] Li W, Zhu X T, Gong S G. Harmonious attention network for person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2285-2294(2018).

[12] Li X, Wang W H, Hu X L et al. Selective kernel networks[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 510-519(2019).

[13] Zhou K Y, Yang Y X, Cavallaro A et al. Omni-scale feature learning for person re-identification[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27- November 2, 2019, Seoul, Korea (South)., 3701-3711(2019).

[14] Chen Y P, Kalantidis Y, Li J S et al. -10-27)[2019-12-26]. https: ∥arxiv., org/abs/1810, 11579(2018).

[15] CholletF. Xception: deep learning with depthwise separable convolutions[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 1800- 1807.

[16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[17] Lin T Y. RoyChowdhury A, Maji S. Bilinear CNN models for fine-grained visual recognition[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1449-1457(2015).

[18] Wang H X, Gong S G. -12-05)[2019-12-26]. https: ∥arxiv., org/abs/1612, 01341(2016).

[19] Zheng Z D, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 3774-3782(2017).

[20] LiW, ZhaoR, XiaoT, et al.DeepReID: deep filter pairing neural network for person re-identification[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE Press, 2014: 152- 159.

[21] Felzenszwalb P F, Girshick R B. McAllester D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1627-1645(2010).

[22] Song C F, Huang Y, Wanli O Y et al. Mask-guided contrastive attention model for person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 1179-1188(2018).

[23] Li D W, Chen X T, Zhang Z et al. Learning deep context-aware features over body and latent parts for person re-identification[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, , 7398-7407(2017).

[24] Chang X B, Hospedales T M, Xiang T. Multi-level factorisation net for person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2109-2118(2018).

[25] Kalayeh M M, Basaran E, Gökmen M et al. Human semantic parsing for person re-identification[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 1062-1071(2018).

[26] Zhong Z, Zheng L, Cao D L et al. Re-ranking person re-identification with k-reciprocal encoding[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 3652-3661(2017).

[27] Li W, Zhu X T. -05-12)[2019-12-26]. https: ∥arxiv., org/abs/1705, 04724(2017).

[28] Sun Y F, Zheng L, Deng W J et al. SVDNet for pedestrian retrieval[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 3820-3828(2017).

[29] Chen Y B, Zhu X T, Gong S G. Person re-identification by deep learning multi-scale representations[C]∥2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, Venice, Italy., 2590-2600(2017).

[30] SandlerM, HowardA, Zhu ML, et al.MobileNetV2: inverted residuals and linear bottlenecks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 4510- 4520.