笔记：Few-shot learning for tackling open-set generalization：
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基于点云的语义分割的应用：场景理解，给点云中每一个点赋予特点的语义标签。（如自动驾驶）
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小样本学习的意义：解决太过于依赖大量标定数据，减少成本；可以提高泛化能力,识别未曾见过的目标。
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paper1:Few-shot 3D Point Cloud Semantic Segmentation
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提出问题：
- rely on large amounts of labeled training data, so they are time-consuming and expensive to collect.
- follow the closed set assumption.(训练集和测试集取自同一label space) ，泛化能力差。
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解决：
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multi-prototype transductive inference method.
- transductive inference: 转导推理；是一种通过观察特点的样本，进而预测特定的测试样本的方法，是一种从特殊到特殊的推理，适合于小样本推理。不同于归纳推理，先从训练样本中学习规则，再用规则判断测试样本。
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architecture：
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embedding network:
- three properties:1.local geometric features; 2.global geometric features; 3. adapt to different few-shot tasks.
- DGGNN: the backbone of feature extractor.(local)
- SAN(self-attention network): generate semantic feature.(global)
- MLP: adapt to different few-shot tasks.
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multi-prototype generation:
- It samples a subset of n seed points from a set of support points in one class using the farthest point sampling based on the embedding space.(对support set的每一类样本点farthest points sample，抽取n个seed point)
- The farthest points represent different perspectives of one class. (farthest points sample保证足够的感受野)
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transductive inference:
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use transductive label propagation to construct a graph on the labeled multi-prototypes and the unlabeled query points.(用k-NN建立相关类的图)
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label propagation
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cross-entropy loss function(交叉熵损失函数):
- compute the cross-entropy loss with ground truth labels.
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paper2:What Makes for Effective Few-shot Point Cloud Classification?
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提出问题：
- they require extensive data collection and retraining when dealing with novel classes never seen before.
- It is hard to study from existing 2D methods when migrating to the 3D domain.
- point clouds are more complex and have unorder structure in European space.
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3D point cloud classification
- projection-based: It first converts the irregular points into a representation like voxel, pillar, and then apply typical 2D or 3D CNN to extract features.
- point-based: It can learns point-wise features with multilayer perceptron(MLP) and aggregates global feature with a symmetric function implemented by a max-pooling layer.
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2D few-shot learning
- Metric-based: It focus on learning an embedding space where similar samples pairs are closer, or designing a metric function to compare the feature similarity of samples.
- Optimization-based: It regards meta-learning as an optimization process.
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State-of-the-art 2D FSL on Point Cloud
- compare the metric-based methods and optimization-based methods, and concludes that metric-based methods outperform the optimization-based methods in point cloud scenario.
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Influence of Backbone Architecture on FSL
- select three types of current state-of-the-art 3D point-based networks including Pointwise-based， Convolution-based， Graph-based(DGCNN). One can conclude that the graph-based network DGCNN achieves higher classification accuracy than other networks on these two datasets.
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Cross Instance Adaption (CIA) module
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CIA can be inserted into existing backbones and learning frameworks to learn more discriminative representations for the support set and query set.
Embedding module把support-set和query-set作为输入分别进行特征提取得到他们的prototype，然后再通过CIA模块更新support-set和query-set，然后在特征空间计算每个class prototype和query examples的欧氏距离，最后便可得到损失函数并进行优化。
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Self-Channel Interaction Module: address the issues of subtle inter-class differences.
- 先从embedding space分别由两个线性系数φ和γ得到q向量和k向量，然后通过CIM的双线性变换得到一个channel-wise relation score map - R, 然后进行softmax操作得到权重矩阵R’，最后得到更新的向量v是有R’与开始的特征向量加权和得到，vi越大说明特供信息越大，有利于区分class之间的细小差别。
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Cross-Instance Fusion Module: address high intra-class variances issues
- 首先将support feature和query feature 连结起来得到Z,然后用两个卷积层来解码连结后的特征得到W，将W进行softmax操作得到权值矩阵后与Z点乘来更新support feature和query feature。
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本文还提供了两个适用于3D FSL的数据集：ModelNet40-FS，ShapeNet70-FS
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