{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T19:13:19Z","timestamp":1778699599786,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973245"],"award-info":[{"award-number":["61973245"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,12]]},"DOI":"10.1145\/3394171.3414537","type":"proceedings-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T12:27:38Z","timestamp":1602505658000},"page":"4461-4464","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Benyi","family":"Hu","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren-Jie","family":"Song","sequence":"additional","affiliation":[{"name":"Megvii Research Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiu-Shen","family":"Wei","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazhou","family":"Yao","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehu","family":"Liu","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"R. Arandjelovi\u0107 and A. Zisserman. 2012. Three things everyone should know to improve object retrieval. In CVPR. 2911--2918.  R. Arandjelovi\u0107 and A. Zisserman. 2012. Three things everyone should know to improve object retrieval. In CVPR. 2911--2918.","DOI":"10.1109\/CVPR.2012.6248018"},{"key":"e_1_3_2_2_2_1","unstructured":"A. Babenko and V. Lempitsky. 2015. Aggregating deep convolutional features for image retrieval. In ICCV. 1269--1277.  A. Babenko and V. Lempitsky. 2015. Aggregating deep convolutional features for image retrieval. In ICCV. 1269--1277."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"O. Chum J. Philbin J. Sivic M. Isard and A. Zisserman. 2007. Total recall: Automatic query expansion with a generative feature model for object retrieval. In ICCV. 1--8.  O. Chum J. Philbin J. Sivic M. Isard and A. Zisserman. 2007. Total recall: Automatic query expansion with a generative feature model for object retrieval. In ICCV. 1--8.","DOI":"10.1109\/ICCV.2007.4408891"},{"key":"e_1_3_2_2_4_1","volume-title":"CVPR workshop. 178--186","author":"Fei-Fei L.","unstructured":"L. Fei-Fei , R. Fergus , and P. Perona . 2004. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories . In CVPR workshop. 178--186 . L. Fei-Fei, R. Fergus, and P. Perona. 2004. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In CVPR workshop. 178--186."},{"key":"e_1_3_2_2_5_1","unstructured":"R. Girshick. 2018. YACS. Website. https:\/\/github.com\/rbgirshick\/yacs.  R. Girshick. 2018. YACS. Website. https:\/\/github.com\/rbgirshick\/yacs."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02163027"},{"key":"e_1_3_2_2_7_1","volume-title":"ECCV workshop. 685--701","author":"Kalantidis Y.","unstructured":"Y. Kalantidis , C. Mellina , and S. Osindero . 2016. Cross-dimensional weighting for aggregated deep convolutional features . In ECCV workshop. 685--701 . Y. Kalantidis, C. Mellina, and S. Osindero. 2016. Cross-dimensional weighting for aggregated deep convolutional features. In ECCV workshop. 685--701."},{"key":"e_1_3_2_2_8_1","unstructured":"A. Paszke S. Gross F. Massa A. Lerer J. Bradbury G. Chanan T. Killeen Z. Lin N. Gimelshein L. Antiga A. Desmaison A. K\u00f6pf E. Yang Z. DeVito M. Raison A. Tejani S. Chilamkurthy B. Steiner L. Fang J. Bai and S. Chintala. 2019. PyTorch: An imperative style high-performance deep learning library. In NeurIPS. 8024--8035.  A. Paszke S. Gross F. Massa A. Lerer J. Bradbury G. Chanan T. Killeen Z. Lin N. Gimelshein L. Antiga A. Desmaison A. K\u00f6pf E. Yang Z. DeVito M. Raison A. Tejani S. Chilamkurthy B. Steiner L. Fang J. Bai and S. Chintala. 2019. PyTorch: An imperative style high-performance deep learning library. In NeurIPS. 8024--8035."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"J. Philbin O. Chum M. Isard J. Sivic and A. Zisserman. 2007. Object retrieval with large vocabularies and fast spatial matching. In CVPR. 1--8.  J. Philbin O. Chum M. Isard J. Sivic and A. Zisserman. 2007. Object retrieval with large vocabularies and fast spatial matching. In CVPR. 1--8.","DOI":"10.1109\/CVPR.2007.383172"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"A. Quattoni and A. Torralba. 2009. Recognizing indoor scenes. In CVPR. 413--420.  A. Quattoni and A. Torralba. 2009. Recognizing indoor scenes. In CVPR. 413--420.","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2846566"},{"key":"e_1_3_2_2_12_1","volume-title":"ECCV workshop. 17--35","author":"Ristani E.","unstructured":"E. Ristani , F. Solera , R. Zou , R. Cucchiara , and C. Tomasi . 2016. Performance measures and a data set for multi-target, multi-camera tracking . In ECCV workshop. 17--35 . E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In ECCV workshop. 17--35."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"L. Rossetto G. Ivan T. Claudiu and S. Heiko. 2016. vitrivr: A Flexible Retrieval Stack Supporting Multiple Query Modes for Searching in Multimedia Collections. In ACM MM. 1183--1186.  L. Rossetto G. Ivan T. Claudiu and S. Heiko. 2016. vitrivr: A Flexible Retrieval Stack Supporting Multiple Query Modes for Searching in Multimedia Collections. In ACM MM. 1183--1186.","DOI":"10.1145\/2964284.2973797"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Y. Sun L. Zheng Y. Yang Q. Tian and S. Wang. 2018. Beyond part models: Person retrieval with refined part pooling. In ECCV. 501--518.  Y. Sun L. Zheng Y. Yang Q. Tian and S. Wang. 2018. Beyond part models: Person retrieval with refined part pooling. In ECCV. 501--518.","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"e_1_3_2_2_15_1","unstructured":"G. Tolias R. Sicre and H. J\u00e9gou. 2016. Particular object retrieval with integral max-pooling of CNN activations. In ICLR. 1--12.  G. Tolias R. Sicre and H. J\u00e9gou. 2016. Particular object retrieval with integral max-pooling of CNN activations. In ICLR. 1--12."},{"key":"e_1_3_2_2_16_1","volume-title":"Technical Report CNS-TR-2011-001. California Institute of Technology.","author":"Wah C.","year":"2011","unstructured":"C. Wah , S. Branson , P. Welinder , P. Perona , and S. Belongie . 2011 . The Caltech-UCSD Birds-200--2011 Dataset . Technical Report CNS-TR-2011-001. California Institute of Technology. C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. 2011. The Caltech-UCSD Birds-200--2011 Dataset. Technical Report CNS-TR-2011-001. California Institute of Technology."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"J. Wan D. Wang S. C.-H. Hoi P. Wu J. Zhu Y. Zhang and J. Li. 2014. Deep Learning for Content-Based Image Retrieval: A Comprehensive Study. In ACM MM. 157--166.  J. Wan D. Wang S. C.-H. Hoi P. Wu J. Zhu Y. Zhang and J. Li. 2014. Deep Learning for Content-Based Image Retrieval: A Comprehensive Study. In ACM MM. 157--166.","DOI":"10.1145\/2647868.2654948"},{"key":"e_1_3_2_2_18_1","first-page":"2868","article-title":"Selective convolutional descriptor aggregation for fine-grained image retrieval","volume":"26","author":"Wei X.-S.","year":"2017","unstructured":"X.-S. Wei , J.-H. Luo , J. Wu , and Z.-H. Zhou . 2017 . Selective convolutional descriptor aggregation for fine-grained image retrieval . IEEE TIP , Vol. 26 , 6 (2017), 2868 -- 2881 . X.-S. Wei, J.-H. Luo, J. Wu, and Z.-H. Zhou. 2017. Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE TIP, Vol. 26, 6 (2017), 2868--2881.","journal-title":"IEEE TIP"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"S. Wold K. Esbensen and P. Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems Vol. 2 1--3 (1987) 37--52.  S. Wold K. Esbensen and P. Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems Vol. 2 1--3 (1987) 37--52.","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"J. Xu C. Shi C. Qi C. Wang and B. Xiao. 2018. Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In AAAI. 7436--7443.  J. Xu C. Shi C. Qi C. Wang and B. Xiao. 2018. Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In AAAI. 7436--7443.","DOI":"10.1609\/aaai.v32i1.12231"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"L. Zheng L. Shen L. Tian S. Wang J. Wang and Q. Tian. 2015. Scalable person re-identification: A benchmark. In ICCV. 1116--1124.  L. Zheng L. Shen L. Tian S. Wang J. Wang and Q. Tian. 2015. Scalable person re-identification: A benchmark. In ICCV. 1116--1124.","DOI":"10.1109\/ICCV.2015.133"},{"key":"e_1_3_2_2_22_1","unstructured":"Z. Zheng. 2018. https:\/\/github.com\/layumi\/Person_reID_baseline_pytorch.  Z. Zheng. 2018. https:\/\/github.com\/layumi\/Person_reID_baseline_pytorch."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Z. Zhong L. Zheng D. Cao and S. Li. 2017. Re-ranking person re-identification with k-reciprocal encoding. In CVPR. 1318--1327.  Z. Zhong L. Zheng D. Cao and S. Li. 2017. Re-ranking person re-identification with k-reciprocal encoding. In CVPR. 1318--1327.","DOI":"10.1109\/CVPR.2017.389"}],"event":{"name":"MM '20: The 28th ACM International Conference on Multimedia","location":"Seattle WA USA","acronym":"MM '20","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 28th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3414537","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394171.3414537","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:24Z","timestamp":1750197684000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3414537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":23,"alternative-id":["10.1145\/3394171.3414537","10.1145\/3394171"],"URL":"https:\/\/doi.org\/10.1145\/3394171.3414537","relation":{},"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"2020-10-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}