This paper introduces and studies zero-base generalized few-shot learning
(zero-base GFSL), which is an extreme yet practical version of few-shot
learning problem. Motivated by the cases where base data is not available due
to privacy or ethical issues, the goal of zero-base GFSL is to newly
incorporate the knowledge of few samples of novel classes into a pretrained
model without any samples of base classes. According to our analysis, we
discover the fact that both mean and variance of the weight distribution of
novel classes are not properly established, compared to those of base classes.
The existing GFSL methods attempt to make the weight norms balanced, which we
find helps only the variance part, but discard the importance of mean of
weights particularly for novel classes, leading to the limited performance in
the GFSL problem even with base data. In this paper, we overcome this
limitation by proposing a simple yet effective normalization method that can
effectively control both mean and variance of the weight distribution of novel
classes without using any base samples and thereby achieve a satisfactory
performance on both novel and base classes. Our experimental results somewhat
surprisingly show that the proposed zero-base GFSL method that does not utilize
any base samples even outperforms the existing GFSL methods that make the best
use of base data. Our implementation is available at:
this https URL.

本文介绍和研究了零基础广义少样学习（zero-base GFSL），提出了一种简单而有效的归一化方法，可以有效控制新类的权重分布的均值和方差，实现了对新类和基类的令人满意的性能，并且不使用任何基本样本的零基础 GFSL 方法甚至优于利用基本数据的现有 GFSL 方法。

即使没有基础数据也能实现更好的泛化小样本学习

Better Generalized Few-Shot Learning Even Without Base Data

Prior human parsing models are limited to parsing humans into classes
pre-defined in the training data, which is not flexible to generalize to unseen
classes, e.g., new clothing in fashion analysis. In this paper, we propose a
new problem named one-shot human parsing (OSHP) that requires to parse human
into an open set of reference classes defined by any single reference example.
During training, only base classes defined in the training set are exposed,
which can overlap with part of reference classes. In this paper, we devise a
novel Progressive One-shot Parsing network (POPNet) to address two critical
challenges , i.e., testing bias and small sizes. POPNet consists of two
collaborative metric learning modules named Attention Guidance Module and
Nearest Centroid Module, which can learn representative prototypes for base
classes and quickly transfer the ability to unseen classes during testing,
thereby reducing testing bias. Moreover, POPNet adopts a progressive human
parsing framework that can incorporate the learned knowledge of parent classes
at the coarse granularity to help recognize the descendant classes at the fine
granularity, thereby handling the small sizes issue. Experiments on the ATR-OS
benchmark tailored for OSHP demonstrate POPNet outperforms other representative
one-shot segmentation models by large margins and establishes a strong
baseline. Source code can be found at
this https URL

本文提出了一个名为 OSHP 的新问题，要求将人解析为由任一参考示例定义的开放参考类集，我们设计了一个新颖的渐进式一次性解析网络（POPNet），其中包含两个协作的度量学习模块，可以学习基类的代表原型，并可以在测试期间快速转移能力到未见过的类别，从而减少测试偏差。此外，POPNet 采用一个渐进的人体解析框架，可以在粗粒度上整合父类的学习知识，以识别细粒度的后代类别，从而处理小规模问题。