In LaTeX text recognition using Transformer-based architectures, this paper
identifies certain "bias" issues. For instance, $e-t$ is frequently
misrecognized as $e^{-t}$. This bias stems from the inherent characteristics of
the dataset. To mitigate this bias, we propose a LaTeX printed text recognition
model trained on a mixed dataset of pseudo-formulas and pseudo-text. The model
employs a Swin Transformer as the encoder and a RoBERTa model as the decoder.
Experimental results demonstrate that this approach reduces "bias", enhancing
the accuracy and robustness of text recognition. For clear images, the model
strictly adheres to the image content; for blurred images, it integrates both
image and contextual information to produce reasonable recognition results.

使用基于 Transformer 的架构进行 LaTeX 文本识别，识别到存在的 “偏差” 问题并提出使用混合数据集训练的 LaTeX 打印文本识别模型，该模型在编码器中采用 Swin Transformer，解码器中采用 RoBERTa 模型。实验结果表明，该方法减小了 “偏差”，提高了文本识别的准确性和鲁棒性。对于清晰图像，模型严格遵循图像内容；对于模糊图像，它整合图像和上下文信息以产生合理的识别结果。

不应仅依赖自然语言训练的明确识别

Unambiguous Recognition Should Not Rely Solely on Natural Language  Training

Face images contain a wide variety of attribute information. In this paper,
we propose a generalized framework for joint estimation of ordinal and nominal
attributes based on information sharing. We tackle the correlation problem
between heterogeneous attributes using hard parameter sharing of shallow
features, and trade-off multiple loss functions by considering homoskedastic
uncertainty for each attribute estimation task. This leads to optimal
estimation of multiple attributes of the face and reduces the training cost of
multitask learning. Experimental results on benchmarks with multiple face
attributes show that the proposed approach has superior performance compared to
state of the art. Finally, we discuss the bias issues arising from the proposed
approach in face attribute estimation and validate its feasibility on edge
systems.

基于信息共享，提出了一种广义框架来联合估计面部的序数和名义属性，并通过考虑每个属性估计任务的同方差不确定性来权衡多个损失函数，以实现面部多属性的最优估计，并降低多任务学习的训练成本，与现有方法相比，实验证明所提出的方法具有优越的性能，最后，讨论了面部属性估计的偏差问题，并验证其在边缘系统上的可行性。

利用不确定性加权的多任务学习用于异质面部属性估计

Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous  Face Attribute Estimation

Recent developments in machine learning have shown that successful models do
not rely only on huge amounts of data but the right kind of data. We show in
this paper how this data-centric approach can be facilitated in a decentralized
manner to enable efficient data collection for algorithms. Face detectors are a
class of models that suffer heavily from bias issues as they have to work on a
large variety of different data. We also propose a face detection and
anonymization approach using a hybrid MultiTask Cascaded CNN with FaceNet
Embeddings to benchmark multiple datasets to describe and evaluate the bias in
the models towards different ethnicities, gender, and age groups along with
ways to enrich fairness in a decentralized system of data labeling, correction,
and verification by users to create a robust pipeline for model retraining.

本研究提出了使用混合多任务级联卷积神经网络和 FaceNet 嵌入来消除人脸检测模型对不同种族、性别和年龄群体的偏见，并在用户验证的分散化系统中进行模型重训练。