Traditional shape descriptors have been gradually replaced by convolutional
neural networks due to their superior performance in feature extraction and
classification. The state-of-the-art methods recognize object shapes via image
reconstruction or pixel classification. However , these methods are biased
toward texture information and overlook the essential shape descriptions, thus,
they fail to generalize to unseen shapes. We are the first to propose a fewshot
shape descriptor (FSSD) to recognize object shapes given only one or a few
samples. We employ an embedding module for FSSD to extract
transformation-invariant shape features. Secondly, we develop a dual attention
mechanism to decompose and reconstruct the shape features via learnable shape
primitives. In this way, any shape can be formed through a finite set basis,
and the learned representation model is highly interpretable and extendable to
unseen shapes. Thirdly, we propose a decoding module to include the supervision
of shape masks and edges and align the original and reconstructed shape
features, enforcing the learned features to be more shape-aware. Lastly, all
the proposed modules are assembled into a few-shot shape recognition scheme.
Experiments on five datasets show that our FSSD significantly improves the
shape classification compared to the state-of-the-art under the few-shot
setting.

我们提出了一种少样本形状描述符（FSSD），通过嵌入模块提取具有转换不变性的形状特征，使用可学习的形状原语分解和重构形状特征，还提出解码模块通过形状掩码和边缘对齐原始和重构的形状特征以使其更具形状感知能力，最后将所有模块组装成了一个少样本形状识别方案，实验证明我们的 FSSD 在少样本情况下相比最先进方法明显提升了形状分类。

通过学习深度形状感知特征进行少样本形状识别

Few-shot Shape Recognition by Learning Deep Shape-aware Features

The staleness problem is a well-known problem when working with dynamic data,
due to the absence of events for a long time. Since the memory of the node is
updated only when the node is involved in an event, its memory becomes stale.
Usually, it refers to a lack of events such as a temporal deactivation of a
social account. To overcome the memory staleness problem aggregate information
from the nodes neighbors memory in addition to the nodes memory. Inspired by
that, we design an updated embedding module that inserts the most similar node
in addition to the nodes neighbors. Our method achieved similar results to the
TGN, with a slight improvement. This could indicate a potential improvement
after fine-tuning our hyper-parameters, especially the time threshold, and
using a learnable similarity metric.

本文针对动态数据在不存在事件时导致的记忆陈旧的问题提出了更新的嵌入模块，并且通过使用邻居节点的记忆信息来解决记忆陈旧问题，同时提出将最相似的节点插入到邻居节点中。通过 fine-tuning 模型超参数，特别是时间阈值和学习可学相似度度量标准，我们的方法与 TGN 相比有 slight improvement 。