Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic
because it needs to categorize both seen and novel intents simultaneously.
Previous GFSID methods rely on the episodic learning paradigm, which makes it
hard to extend to a generalized setup as they do not explicitly learn the
classification of seen categories and the knowledge of seen intents. To address
the dilemma, we propose to convert the GFSID task into the class incremental
learning paradigm. Specifically, we propose a two-stage learning framework,
which sequentially learns the knowledge of different intents in various periods
via prompt learning. And then we exploit prototypes for categorizing both seen
and novel intents. Furthermore, to achieve the transfer knowledge of intents in
different stages, for different scenarios we design two knowledge preservation
methods which close to realistic applications. Extensive experiments and
detailed analyses on two widely used datasets show that our framework based on
the class incremental learning paradigm achieves promising performance.

我们提出了一种基于类增量学习范式的广义少样本意图检测框架，通过阶段性学习以及原型的利用来同时对已见和新意图进行分类。通过两种不同的知识保留方法来实现不同阶段间的意图迁移，实验结果表明我们的框架具有良好的性能。

基于知识记忆原型的通用少样本意图检测的提示学习

Prompt Learning With Knowledge Memorizing Prototypes For Generalized  Few-Shot Intent Detection

Handwritten signature verification is a significant biometric verification
method widely acknowledged by legal and financial institutions. However, the
development of automatic signature verification systems poses challenges due to
inter-writer similarity, intra-writer variations, and the limited number of
signature samples. To address these challenges, we propose a multiscale global
and regional feature learning network (MGRNet) with the co-tuplet loss, a new
metric learning loss, for offline handwritten signature verification. MGRNet
jointly learns global and regional information from various spatial scales and
integrates it to generate discriminative features. Consequently, it can capture
overall signature stroke information while detecting detailed local differences
between genuine and skilled-forged signatures. To enhance the discriminative
capability of our network further, we propose the co-tuplet loss, which
simultaneously considers multiple positive and negative examples to learn
distance metrics. By dealing with inter-writer similarity and intra-writer
variations and focusing on informative examples, the co-tuplet loss addresses
the limitations of typical metric learning losses. Additionally, we develop
HanSig, a large-scale Chinese signature dataset, to facilitate the development
of robust systems for this script. The dataset is available at
this https URL Experimental results on four benchmark
datasets in different languages demonstrate the promising performance of our
method in comparison to state-of-the-art approaches.

离线手写签名验证中，我们提出了多尺度全局和区域特征学习网络（MGRNet）和共同五元损失（co-tuplet loss）来解决签名相似性、变化性以及样本有限的挑战，同时通过 HanSig 数据集提供了一个稳健系统的大规模中文签名数据集，并且与其他最先进的方法相比，表现出了良好的性能。