@inproceedings{89cd75fafc964eebaf4e01662072611f,
title = "Combine coarse and fine cues: Multi-grained fusion network for video-based person re-identification",
abstract = "Video-based person re-identification aims to precisely match video sequences of pedestrian across non-overlapped cameras. Existing methods deal with this task by encoding each frame and aggregating them along time. In order to increase the discriminative ability of video features, we propose an end-to-end framework called Multi-grained Fusion Network (MGFN) which aims to keep both global and local information by combining frame-level representations with different granularities. The final video features are generated by aggregating multi-grained representations on both spatial and temporal. Experiments indicate our method achieves excellent performance on three widely used datasets named PRID-2011, iLIDS-VID, and MARS. Especially on MARS, MGFN surpass state-of-the-art result by 11.5%.",
keywords = "Multi-grained feature, Multi-grained fusion network, Part-based model, Video-based person re-identification",
author = "Chao Li and Lei Liu and Kai Lv and Hao Sheng and Wei Ke",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018 ; Conference date: 17-08-2018 Through 19-08-2018",
year = "2018",
doi = "10.1007/978-3-319-99365-2_16",
language = "English",
isbn = "9783319993645",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "177--184",
editor = "Fausto Giunchiglia and Weiru Liu and Bo Yang",
booktitle = "Knowledge Science, Engineering and Management - 11th International Conference, KSEM 2018, Proceedings",
address = "Germany",
}