The exploration of few-shot tabular learning becomes imperative. Tabular data
is a versatile representation that captures diverse information, yet it is not
exempt from limitations, property of data and model size. Labeling extensive
tabular data can be challenging, and it may not be feasible to capture every
important feature. Few-shot tabular learning, however, remains relatively
unexplored, primarily due to scarcity of shared information among independent
datasets and the inherent ambiguity in defining boundaries within tabular data.
To the best of our knowledge, no meaningful and unrestricted few-shot tabular
learning techniques have been developed without imposing constraints on the
dataset. In this paper, we propose an innovative framework called TablEye,
which aims to overcome the limit of forming prior knowledge for tabular data by
adopting domain transformation. It facilitates domain transformation by
generating tabular images, which effectively conserve the intrinsic semantics
of the original tabular data. This approach harnesses rigorously tested
few-shot learning algorithms and embedding functions to acquire and apply prior
knowledge. Leveraging shared data domains allows us to utilize this prior
knowledge, originally learned from the image domain. Specifically, TablEye
demonstrated a superior performance by outstripping the TabLLM in a 4-shot task
with a maximum 0.11 AUC and a STUNT in a 1- shot setting, where it led on
average by 3.17% accuracy.

本文介绍了一种名为 TablEye 的创新框架，通过采用域转换来克服形成表格数据的先验知识的限制，它通过生成表格图像有效地保留原始表格数据的内在语义，并利用共享数据域来获取和应用先验知识。TablEye 在 4-shot 任务中超过了 TabLLM，最大 AUC 为 0.11，以及在 1-shot 环境中引领了平均 3.17％的准确度。

TablEye: 透过图像镜头看小型表格

TablEye: Seeing small Tables through the Lens of Images

Recently, vehicle similarity learning, also called re-identification (ReID),
has attracted significant attention in computer vision. Several algorithms have
been developed and obtained considerable success. However, most existing
methods have unpleasant performance in the hazy scenario due to poor
visibility. Though some strategies are possible to resolve this problem, they
still have room to be improved due to the limited performance in real-world
scenarios and the lack of real-world clear ground truth. Thus, to resolve this
problem, inspired by CycleGAN, we construct a training paradigm called
\textbf{RVSL} which integrates ReID and domain transformation techniques. The
network is trained on semi-supervised fashion and does not require to employ
the ID labels and the corresponding clear ground truths to learn hazy vehicle
ReID mission in the real-world haze scenes. To further constrain the
unsupervised learning process effectively, several losses are developed.
Experimental results on synthetic and real-world datasets indicate that the
proposed method can achieve state-of-the-art performance on hazy vehicle ReID
problems. It is worth mentioning that although the proposed method is trained
without real-world label information, it can achieve competitive performance
compared to existing supervised methods trained on complete label information.

本篇研究提出了一种基于 CycleGAN 的训练范式 ——RVSL，它整合了 ReID 和域变换技术，并在半监督的训练方式下解决了车辆 ReID 在真实世界雾霾场景中的问题。实验结果表明，该方法在雾霾场景下的车辆 ReID 比现有方法表现更好。