In Generalized Few-shot Segmentation (GFSS), a model is trained with a large
corpus of base class samples and then adapted on limited samples of novel
classes. This paper focuses on the relevance between base and novel classes,
and improves GFSS in two aspects: 1) mining the similarity between base and
novel classes to promote the learning of novel classes, and 2) mitigating the
class imbalance issue caused by the volume difference between the support set
and the training set. Specifically, we first propose a similarity transition
matrix to guide the learning of novel classes with base class knowledge. Then,
we leverage the Label-Distribution-Aware Margin (LDAM) loss and Transductive
Inference to the GFSS task to address the problem of class imbalance as well as
overfitting the support set. In addition, by extending the probability
transition matrix, the proposed method can mitigate the catastrophic forgetting
of base classes when learning novel classes. With a simple training phase, our
proposed method can be applied to any segmentation network trained on base
classes. We validated our methods on the adapted version of OpenEarthMap.
Compared to existing GFSS baselines, our method excels them all from 3% to 7%
and ranks second in the OpenEarthMap Land Cover Mapping Few-Shot Challenge at
the completion of this paper. Code:
this https URL

在广义 Few-shot 分割（GFSS）中，通过使用大量基本类别样本对模型进行训练，再对有限的新类别样本进行调整。本文主要关注基本类别和新类别之间的相关性，并在两个方面对 GFSS 进行改进：1）利用基本类别与新类别之间的相似性促进新类别的学习；2）通过缓解支持集和训练集之间的体积差异引起的类别不平衡问题。通过提出相似性转换矩阵指导基于基本类别的知识学习新类别，以及利用 Label-Distribution-Aware Margin（LDAM）损失和 Transductive Inference 来解决类别不平衡和过拟合支持集的问题。此外，通过扩展概率转换矩阵，该方法可以在学习新类别时减轻基本类别的灾难性遗忘。我们通过在 OpenEarthMap 的适应版本上验证了我们的方法。与现有的 GFSS 基线相比，我们的方法在 OpenEarthMap Land Cover Mapping Few-Shot Challenge 中的排名从 3％到 7％不等，并在本文完成时排名第二。

类相似度转换：从广义少样本分割中解耦类相似度和不平衡

Class Similarity Transition: Decoupling Class Similarities and Imbalance  from Generalized Few-shot Segmentation

Few-shot learning is proposed to tackle the problem of scarce training data
in novel classes. However, prior works in instance-level few-shot learning have
paid less attention to effectively utilizing the relationship between
categories. In this paper, we exploit the hierarchical information to leverage
discriminative and relevant features of base classes to effectively classify
novel objects. These features are extracted from abundant data of base classes,
which could be utilized to reasonably describe classes with scarce data.
Specifically, we propose a novel superclass approach that automatically creates
a hierarchy considering base and novel classes as fine-grained classes for
few-shot instance segmentation (FSIS). Based on the hierarchical information,
we design a novel framework called Soft Multiple Superclass (SMS) to extract
relevant features or characteristics of classes in the same superclass. A new
class assigned to the superclass is easier to classify by leveraging these
relevant features. Besides, in order to effectively train the
hierarchy-based-detector in FSIS, we apply the label refinement to further
describe the associations between fine-grained classes. The extensive
experiments demonstrate the effectiveness of our method on FSIS benchmarks.
Code is available online.

本文提出了一种利用层次信息进行分类的几乎无监督的实例级别小样本学习方法，称为 Soft Multiple Superclass (SMS)。该方法采用了一种新的超类方法来自动创建基本和新的类的层次结构，关注基本类别的相关特征以有效分类新对象，并将标签细化来进一步描述细粒度类别之间的关联。在 FSIS 基准测试中，广泛的实验表明了该方法的有效性。