Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support
features for prototype generation, neglecting the specific requirements of the
query. To address this, we present the Query-guided Prototype Evolution Network
(QPENet), a new method that integrates query features into the generation
process of foreground and background prototypes, thereby yielding customized
prototypes attuned to specific queries. The evolution of the foreground
prototype is accomplished through a \textit{support-query-support} iterative
process involving two new modules: Pseudo-prototype Generation (PPG) and Dual
Prototype Evolution (DPE). The PPG module employs support features to create an
initial prototype for the preliminary segmentation of the query image,
resulting in a pseudo-prototype reflecting the unique needs of the current
query. Subsequently, the DPE module performs reverse segmentation on support
images using this pseudo-prototype, leading to the generation of evolved
prototypes, which can be considered as custom solutions. As for the background
prototype, the evolution begins with a global background prototype that
represents the generalized features of all training images. We also design a
Global Background Cleansing (GBC) module to eliminate potential adverse
components mirroring the characteristics of the current foreground class.
Experimental results on the PASCAL-$5^i$ and COCO-$20^i$ datasets attest to the
substantial enhancements achieved by QPENet over prevailing state-of-the-art
techniques, underscoring the validity of our ideas.

通过将查询特征整合到前景和背景原型的生成过程中，Query-guided Prototype Evolution Network (QPENet) 提供了与特定查询相适应的定制原型，通过一系列的模块和过程实现了 Few-Shot Segmentation 的显著性能增强。

基于查询引导的原型演化网络用于少样本分割

Query-guided Prototype Evolution Network for Few-Shot Segmentation

In this paper we reformulate few-shot classification as a reconstruction
problem in latent space. The ability of the network to reconstruct a query
feature map from support features of a given class predicts membership of the
query in that class. We introduce a novel mechanism for few-shot classification
by regressing directly from support features to query features in closed form,
without introducing any new modules or large-scale learnable parameters. The
resulting Feature Map Reconstruction Networks are both more performant and
computationally efficient than previous approaches. We demonstrate consistent
and substantial accuracy gains on four fine-grained benchmarks with varying
neural architectures. Our model is also competitive on the non-fine-grained
mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

本文提出了一种在潜在空间中将 few-shot 分类问题重新表述为重建问题的方法。通过从给定类别的支持特征到查询特征的直接回归，无需引入新的模块或大规模可学习参数，我们引入了一种新的机制进行 Few-shot 分类，该机制被称为特征映射重建网络，它比以前的方法更有效和更高效。我们在四种细粒度基准测试和两种非细粒度基准测试上验证了这个方法的一致和显著的准确性增益。

特征图重构网络在小样本分类中的应用

Few-Shot Classification with Feature Map Reconstruction Networks

In this paper, a novel learning paradigm is presented to automatically
identify groups of informative and correlated features from very high
dimensions. Specifically, we explicitly incorporate correlation measures as
constraints and then propose an efficient embedded feature selection method
using recently developed cutting plane strategy. The benefits of the proposed
algorithm are two-folds. First, it can identify the optimal discriminative and
uncorrelated feature subset to the output labels, denoted here as Support
Features, which brings about significant improvements in prediction performance
over other state of the art feature selection methods considered in the paper.
Second, during the learning process, the underlying group structures of
correlated features associated with each support feature, denoted as Affiliated
Features, can also be discovered without any additional cost. These affiliated
features serve to improve the interpretations on the learning tasks. Extensive
empirical studies on both synthetic and very high dimensional real-world
datasets verify the validity and efficiency of the proposed method.

本文提出了一种新的学习范式，以自动从非常高的维度中识别出信息丰富且相关的特征组。通过利用相关度量作为约束条件，我们提出了一种有效的嵌入式特征选择方法，可以识别出最优的有区分性而且不相关的特征子集，即所谓的支持特征。在学习过程中，还可以发现与每个支持特征相关联的相关特征的基础组结构，即附属特征，而不需要任何额外成本。 在合成和高维真实世界数据集上的广泛实证研究验证了所提出的方法的有效性和效率。