Prototype-based classification is a classical method in machine learning, and
recently it has achieved remarkable success in semi-supervised semantic
segmentation. However, the current approach isolates the prototype
initialization process from the main training framework, which appears to be
unnecessary. Furthermore, while the direct use of K-Means algorithm for
prototype generation has considered rich intra-class variance, it may not be
the optimal solution for the classification task. To tackle these problems, we
propose a novel boundary-refined prototype generation (BRPG) method, which is
incorporated into the whole training framework. Specifically, our approach
samples and clusters high- and low-confidence features separately based on a
confidence threshold, aiming to generate prototypes closer to the class
boundaries. Moreover, an adaptive prototype optimization strategy is introduced
to make prototype augmentation for categories with scattered feature
distributions. Extensive experiments on the PASCAL VOC 2012 and Cityscapes
datasets demonstrate the superiority and scalability of the proposed method,
outperforming the current state-of-the-art approaches. The code is available at
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我们提出了一种新的边界优化类原型生成（BRPG）方法，将其纳入整个训练框架中，该方法在 PASCAL VOC 2012 和 Cityscapes 数据集上的实验表明，它优于当前最先进的方法。

边界精化的原型生成：半监督语义分割的通用端到端范式

Boundary-Refined Prototype Generation: A General End-to-End Paradigm for  Semi-Supervised Semantic Segmentation

Meta-learning extracts common knowledge from learning different tasks and
uses it for unseen tasks. It can significantly improve tasks that suffer from
insufficient training data, e.g., few shot learning. In most meta-learning
methods, tasks are implicitly related by sharing parameters or optimizer. In
this paper, we show that a meta-learner that explicitly relates tasks on a
graph describing the relations of their output dimensions (e.g., classes) can
significantly improve few shot learning. The graph's structure is usually free
or cheap to obtain but has rarely been explored in previous works. We develop a
novel meta-learner of this type for prototype-based classification, in which a
prototype is generated for each class, such that the nearest neighbor search
among the prototypes produces an accurate classification. The meta-learner,
called "Gated Propagation Network (GPN)", learns to propagate messages between
prototypes of different classes on the graph, so that learning the prototype of
each class benefits from the data of other related classes. In GPN, an
attention mechanism aggregates messages from neighboring classes of each class,
with a gate choosing between the aggregated message and the message from the
class itself. We train GPN on a sequence of tasks from many-shot to few shot
generated by subgraph sampling. During training, it is able to reuse and update
previously achieved prototypes from the memory in a life-long learning cycle.
In experiments, under different training-test discrepancy and test task
generation settings, GPN outperforms recent meta-learning methods on two
benchmark datasets. The code of GPN and dataset generation is available at
this https URL

本文提出一种基于显式任务关系图的原型分类的元学习算法，称为门控传播网络（GPN）。GPN 在学习原型信息时，利用关系图中的多个相关任务的信息进行消息传递和选择性聚合，并在多种场景下实验证明其优于现有元学习方法。