Few-shot hyperspectral image classification aims to identify the classes of
each pixel in the images by only marking few of these pixels. And in order to
obtain the spatial-spectral joint features of each pixel, the fixed-size
patches centering around each pixel are often used for classification. However,
observing the classification results of existing methods, we found that
boundary patches corresponding to the pixels which are located at the boundary
of the objects in the hyperspectral images, are hard to classify. These
boundary patchs are mixed with multi-class spectral information. Inspired by
this, we propose to augment the prototype network with TransMix for few-shot
hyperspectrial image classification(APNT). While taking the prototype network
as the backbone, it adopts the transformer as feature extractor to learn the
pixel-to-pixel relation and pay different attentions to different pixels. At
the same time, instead of directly using the patches which are cut from the
hyperspectral images for training, it randomly mixs up two patches to imitate
the boundary patches and uses the synthetic patches to train the model, with
the aim to enlarge the number of hard training samples and enhance their
diversity. And by following the data agumentation technique TransMix, the
attention returned by the transformer is also used to mix up the labels of two
patches to generate better labels for synthetic patches. Compared with existing
methods, the proposed method has demonstrated sate of the art performance and
better robustness for few-shot hyperspectral image classification in our
experiments.

通过引入 TransMix 的原型网络对少样本高光谱图像分类进行增强，该方法在像素级关系和像素级注意力方面进行转换器特征提取，使用合成边界数据增强多样性和用于训练模型，证明了在少样本高光谱图像分类方面具有尖端性能和更好的鲁棒性。

利用 TransMix 增强原型网络进行少样本高光谱图像分类

Augmenting Prototype Network with TransMix for Few-shot Hyperspectral  Image Classification

Few-Shot Relation Extraction aims at predicting the relation for a pair of
entities in a sentence by training with a few labelled examples in each
relation. Some recent works have introduced relation information (i.e.,
relation labels or descriptions) to assist model learning based on Prototype
Network. However, most of them constrain the prototypes of each relation class
implicitly with relation information, generally through designing complex
network structures, like generating hybrid features, combining with contrastive
learning or attention networks. We argue that relation information can be
introduced more explicitly and effectively into the model. Thus, this paper
proposes a direct addition approach to introduce relation information.
Specifically, for each relation class, the relation representation is first
generated by concatenating two views of relations (i.e., [CLS] token embedding
and the mean value of embeddings of all tokens) and then directly added to the
original prototype for both train and prediction. Experimental results on the
benchmark dataset FewRel 1.0 show significant improvements and achieve
comparable results to the state-of-the-art, which demonstrates the
effectiveness of our proposed approach. Besides, further analyses verify that
the direct addition is a much more effective way to integrate the relation
representations and the original prototypes.

本文提出了一种新的直接加法方法来更加明确有效地将关系信息引入 Few-Shot 关系提取模型，该方法通过连接两种关系视图来生 成关系表示，并将其直接添加到原型中进行训练和预测，在 FewRel 1.0 基准数据集上实现了显著的改进，并且与最先进的结果相当。