Few-shot Intent Detection is challenging due to the scarcity of available
annotated utterances. Although recent works demonstrate that multi-level
matching plays an important role in transferring learned knowledge from seen
training classes to novel testing classes, they rely on a static similarity
measure and overly fine-grained matching components. These limitations inhibit
generalizing capability towards Generalized Few-shot Learning settings where
both seen and novel classes are co-existent. In this paper, we propose a novel
Semantic Matching and Aggregation Network where semantic components are
distilled from utterances via multi-head self-attention with additional dynamic
regularization constraints. These semantic components capture high-level
information, resulting in more effective matching between instances. Our
multi-perspective matching method provides a comprehensive matching measure to
enhance representations of both labeled and unlabeled instances. We also
propose a more challenging evaluation setting that considers classification on
the joint all-class label space. Extensive experimental results demonstrate the
effectiveness of our method. Our code and data are publicly available.

本文提出了一种基于多头自注意力和动态正则化约束的语义匹配聚合网络，为 few-shot 意图检测任务提供了更为有效的匹配方法，并在进行了广泛实验后证明了我们方法的有效性。

动态语义匹配与聚合网络用于少样本意图检测

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent  Detection

Transferring learned models to novel tasks is a challenging problem,
particularly if only very few labeled examples are available. Although this
few-shot learning setup has received a lot of attention recently, most proposed
methods focus on discriminating novel classes only. Instead, we consider the
extended setup of generalized few-shot learning (GFSL), where the model is
required to perform classification on the joint label space consisting of both
previously seen and novel classes. We propose a graph-based framework that
explicitly models relationships between all seen and novel classes in the joint
label space. Our model Graph-convolutional Global Prototypical Networks (GcGPN)
incorporates these inter-class relations using graph-convolution in order to
embed novel class representations into the existing space of previously seen
classes in a globally consistent manner. Our approach ensures both fast
adaptation and global discrimination, which is the major challenge in GFSL. We
demonstrate the benefits of our model on two challenging benchmark datasets.

本文提出一种基于图卷积的全局原型网络（GcGPN）的图形框架，该框架显式地模拟了联合标签空间中所有已看到和新类别之间的关系，以在全局一致的方式下将新类别嵌入到以前看到的所有类别的现有空间中，为广义小样本学习（GFSL）中的快速适应和全局区分提供了解决方案。

关系推广少样本学习

Relational Generalized Few-Shot Learning

Object recognition in the real-world requires handling long-tailed or even
open-ended data. An ideal visual system needs to recognize the populated head
visual concepts reliably and meanwhile efficiently learn about emerging new
tail categories with a few training instances. Class-balanced many-shot
learning and few-shot learning tackle one side of this problem, by either
learning strong classifiers for head or learning to learn few-shot classifiers
for the tail. In this paper, we investigate the problem of generalized few-shot
learning (GFSL) -- a model during the deployment is required to learn about
tail categories with few shots and simultaneously classify the head classes. We
propose the ClAssifier SynThesis LEarning (CASTLE), a learning framework that
learns how to synthesize calibrated few-shot classifiers in addition to the
multi-class classifiers of head classes with a shared neural dictionary,
shedding light upon the inductive GFSL. Furthermore, we propose an adaptive
version of CASTLE (ACASTLE) that adapts the head classifiers conditioned on the
incoming tail training examples, yielding a framework that allows effective
backward knowledge transfer. As a consequence, ACASTLE can handle GFSL with
classes from heterogeneous domains effectively. CASTLE and ACASTLE demonstrate
superior performances than existing GFSL algorithms and strong baselines on
MiniImageNet as well as TieredImageNet datasets. More interestingly, they
outperform previous state-of-the-art methods when evaluated with standard
few-shot learning criteria.

这篇论文研究了在物体识别中应对长尾或无限制问题的方法，提出了一种名为 CASTLE 和 ACASTLE 的模型，可以同时识别常见类别并学习少量样本的新出现类别，且可以在多领域中应用。这两种模型在 MiniImageNet 和 TieredImageNet 数据集上的表现优于现有的 GFSL 和强基准方法，且实验结果表明在标准的几种情况下，这两种方法的表现均优于以前的 最新方法。