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Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

Abstract

Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary – the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a random jigsaw patch generator that encourages the network to learn features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and show the proposed method consistently outperforms existing alternatives or delivers competitive results. The code is available at https://github.com/PRIS-CV/PMG-Progressive-Multi-Granularity-Training.

Notes

Acknowledgement

This work was supported in part by the National Key R&D Program of China under Grant 2019YFF0303300 and under Subject II No. 2019YFF0303302, in part by the National Natural Science Foundation of China under Grant 61773071, 61922015, and U19B2036, in part by Beijing Academy of Artificial Intelligence (BAAI) under Grant BAAI2020ZJ0204, in part by the Beijing Nova Program Interdisciplinary Cooperation Project under Grant Z191100001119140, in part by the National Science and Technology Major Program of the Ministry of Science and Technology under Grant 2018ZX03001031, in part by the Key Program of Beijing Municipal Natural Science Foundation under Grant L172030, in part by MoE-CMCC Artificial Intelligence Project No. MCM20190701, in part by the scholarship from China Scholarship Council (CSC) under Grant CSC No. 201906470049, and in part by the BUPT Excellent Ph.D. Students Foundation No. CX2020105 and No. CX2019109.

Supplementary material

504476_1_En_10_MOESM1_ESM.zip (7 kb)
Supplementary material 1 (zip 6 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Pattern Recognition and Intelligent System Laboratory, School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.SketchX, CVSSPUniversity of SurreyGuildfordUK

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