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Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification

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 consistent block convolution that encourages the network to learn the category-consistent features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and demonstrate the proposed method consistently outperforms existing alternatives or delivers competitive results. Codes are available at https://github.com/PRIS-CV/PMG-V2 .

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.12 Part 2 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 9521-9535

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORK
FINE-GRAINED VISUAL CLASSIFICATION
PROGRESSIVE TRAINING
CONSISTENCY CONSTRAINT

Info Detail Spesifik

10.1109/TPAMI.2021.3126668

Pernyataan Tanggungjawab

Ruoyi Du, Jiyang Xie, Zhanyu Ma, Dongliang Chang, Yi-Zhe Song, Jun Guo

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