Existing long-tailed classification (LT) methods only focus on tackling the
class-wise imbalance that head classes have more samples than tail classes, but
overlook the attribute-wise imbalance. In fact, even if the class is balanced,
samples within each class may still be long-tailed due to the varying
attributes. Note that the latter is fundamentally more ubiquitous and
challenging than the former because attributes are not just implicit for most
datasets, but also combinatorially complex, thus prohibitively expensive to be
balanced. Therefore, we introduce a novel research problem: Generalized
Long-Tailed classification (GLT), to jointly consider both kinds of imbalances.
By "generalized", we mean that a GLT method should naturally solve the
traditional LT, but not vice versa. Not surprisingly, we find that most
class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT
and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment
of class distribution while neglecting to learn attribute-invariant features.
To this end, we propose an Invariant Feature Learning (IFL) method as the first
strong baseline for GLT. IFL first discovers environments with divergent
intra-class distributions from the imperfect predictions and then learns
invariant features across them. Promisingly, as an improved feature backbone,
IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and
ensemble. Codes and benchmarks are available on Github:
this https URL

提出了一个新的研究问题：综合考虑类别和属性两个方面的不平衡问题，介绍了一种关于属性无关特征学习的强基线方法（IFL），并发现很多现有的类别不平衡方法在对抗属性级别上不如此方法表现的好。

面向广义长尾分类的不变特征学习

Invariant Feature Learning for Generalized Long-Tailed Classification

Despite various methods are proposed to make progress in pedestrian attribute
recognition, a crucial problem on existing datasets is often neglected, namely,
a large number of identical pedestrian identities in train and test set, which
is not consistent with practical application. Thus, images of the same
pedestrian identity in train set and test set are extremely similar, leading to
overestimated performance of state-of-the-art methods on existing datasets. To
address this problem, we propose two realistic datasets
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot
setting of pedestrian identities based on PETA and RAPv2 datasets. Furthermore,
compared to our strong baseline method, we have observed that recent
state-of-the-art methods can not make performance improvement on PETA, RAPv2,
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$}. Thus, through solving
the inherent attribute imbalance in pedestrian attribute recognition, an
efficient method is proposed to further improve the performance. Experiments on
existing and proposed datasets verify the superiority of our method by
achieving state-of-the-art performance.

提出两个现实数据集 PETA 和 RAPv2，通过解决行人属性识别中固有属性不平衡问题，提出高效方法进一步提高性能，实验证明该方法优越性并取得最先进的性能。