Transductive Few-Shot Learning (TFSL) has recently attracted increasing
attention since it typically outperforms its inductive peer by leveraging
statistics of query samples. However, previous TFSL methods usually encode
uniform prior that all the classes within query samples are equally likely,
which is biased in imbalanced TFSL and causes severe performance degradation.
Given this pivotal issue, in this work, we propose a novel Conditional
Transport (CT) based imbalanced TFSL model called {\textbf P}rototypes-oriented
{\textbf U}nbiased {\textbf T}ransfer {\textbf M}odel (PUTM) to fully exploit
unbiased statistics of imbalanced query samples, which employs forward and
backward navigators as transport matrices to balance the prior of query samples
per class between uniform and adaptive data-driven distributions. For
efficiently transferring statistics learned by CT, we further derive a closed
form solution to refine prototypes based on MAP given the learned navigators.
The above two steps of discovering and transferring unbiased statistics follow
an iterative manner, formulating our EM-based solver.
Experimental results on four standard benchmarks including miniImageNet,
tieredImageNet, CUB, and CIFAR-FS demonstrate superiority of our model in
class-imbalanced generalization.

我们提出了一种基于条件传输的不平衡转导式少样本学习模型（PUTM），通过使用前向和后向导航器作为传输矩阵来平衡查询样本中每个类别的先验概率，以充分利用不平衡查询样本的无偏统计信息，并在四个标准基准测试中证明了我们模型在类别不平衡的泛化方面的优越性。

基于原型的条件转移的半监督少样本学习

Prototypes-oriented Transductive Few-shot Learning with Conditional  Transport

Multi-label image classification is a prediction task that aims to identify
more than one label from a given image. This paper considers the semantic
consistency of the latent space between the visual patch and linguistic label
domains and introduces the conditional transport (CT) theory to bridge the
acknowledged gap. While recent cross-modal attention-based studies have
attempted to align such two representations and achieved impressive
performance, they required carefully-designed alignment modules and extra
complex operations in the attention computation. We find that by formulating
the multi-label classification as a CT problem, we can exploit the interactions
between the image and label efficiently by minimizing the bidirectional CT
cost. Specifically, after feeding the images and textual labels into the
modality-specific encoders, we view each image as a mixture of patch embeddings
and a mixture of label embeddings, which capture the local region features and
the class prototypes, respectively. CT is then employed to learn and align
those two semantic sets by defining the forward and backward navigators.
Importantly, the defined navigators in CT distance model the similarities
between patches and labels, which provides an interpretable tool to visualize
the learned prototypes. Extensive experiments on three public image benchmarks
show that the proposed model consistently outperforms the previous methods. Our
code is available at this https URL

本研究论文探讨了多标签图像分类问题，介绍了条件传输理论以弥合视觉补丁和语义标签之间的潜在空间的语义一致性差距，并将多标签分类问题重新表示为条件传输问题。实验证明，该模型在三个公共图像基准任务中 consistently outperforms the previous methods.