Fine-grained visual classification (FGVC) is much more challenging than
traditional classification tasks due to the inherently subtle intra-class
object variations. Recent works mainly tackle this problem by focusing on how
to locate the most discriminative parts, more complementary parts, and parts of
various granularities. However, less effort has been placed to which
granularities are the most discriminative and how to fuse information cross
multi-granularity. In this work, we propose a novel framework for fine-grained
visual classification to tackle these problems. 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 obtain state-of-the-art
performances on several standard FGVC benchmark datasets, where the proposed
method consistently outperforms existing methods or delivers competitive
results. The code will be available at
this https URL

本文提出了一种新颖的细粒度视觉分类框架，其中包括渐进式训练策略和随机拼图补丁生成器，以有效地融合不同粒度的特征，并在多个标准的细粒度视觉分类数据集上取得了最先进的性能表现。

通过逐步多粒度拼图块训练进行细粒度视觉分类

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

Fine-grained visual categorization (FGVC) is to categorize objects into
subordinate classes instead of basic classes. One major challenge in FGVC is
the co-occurrence of two issues: 1) many subordinate classes are highly
correlated and are difficult to distinguish, and 2) there exists the large
intra-class variation (e.g., due to object pose). This paper proposes to
explicitly address the above two issues via distance metric learning (DML). DML
addresses the first issue by learning an embedding so that data points from the
same class will be pulled together while those from different classes should be
pushed apart from each other; and it addresses the second issue by allowing the
flexibility that only a portion of the neighbors (not all data points) from the
same class need to be pulled together. However, feature representation of an
image is often high dimensional, and DML is known to have difficulty in dealing
with high dimensional feature vectors since it would require $\mathcal{O}(d^2)$
for storage and $\mathcal{O}(d^3)$ for optimization. To this end, we proposed a
multi-stage metric learning framework that divides the large-scale high
dimensional learning problem to a series of simple subproblems, achieving
$\mathcal{O}(d)$ computational complexity. The empirical study with FVGC
benchmark datasets verifies that our method is both effective and efficient
compared to the state-of-the-art FGVC approaches.

本研究提出了一种多阶段度量学习框架，针对细粒度视觉分类中高维特征向量的学习问题，通过距离度量学习解决了相互关联且难以区分的子类别和大量内部类差异等问题，并在 FGVC 基准数据集上取得了显著的性能提升。