The Mixup method has proven to be a powerful data augmentation technique in
Computer Vision, with many successors that perform image mixing in a guided
manner. One of the interesting research directions is transferring the
underlying Mixup idea to other domains, e.g. Natural Language Processing (NLP).
Even though there already exist several methods that apply Mixup to textual
data, there is still room for new, improved approaches. In this work, we
introduce AttentionMix, a novel mixing method that relies on attention-based
information. While the paper focuses on the BERT attention mechanism, the
proposed approach can be applied to generally any attention-based model.
AttentionMix is evaluated on 3 standard sentiment classification datasets and
in all three cases outperforms two benchmark approaches that utilize Mixup
mechanism, as well as the vanilla BERT method. The results confirm that the
attention-based information can be effectively used for data augmentation in
the NLP domain.

AttentionMix 是一种基于注意力的新混合方法，适用于自然语言处理领域，通过评估在三个标准情感分类数据集上的表现，证实了基于注意力的信息在数据增强中的有效性。

AttentionMix: 借助 BERT 注意机制的数据增强方法

AttentionMix: Data augmentation method that relies on BERT attention  mechanism

Data augmentation is now an essential part of the image training process, as
it effectively prevents overfitting and makes the model more robust against
noisy datasets. Recent mixing augmentation strategies have advanced to generate
the mixup mask that can enrich the saliency information, which is a supervisory
signal. However, these methods incur a significant computational burden to
optimize the mixup mask. From this motivation, we propose a novel
saliency-aware mixup method, GuidedMixup, which aims to retain the salient
regions in mixup images with low computational overhead. We develop an
efficient pairing algorithm that pursues to minimize the conflict of salient
regions of paired images and achieve rich saliency in mixup images. Moreover,
GuidedMixup controls the mixup ratio for each pixel to better preserve the
salient region by interpolating two paired images smoothly. The experiments on
several datasets demonstrate that GuidedMixup provides a good trade-off between
augmentation overhead and generalization performance on classification
datasets. In addition, our method shows good performance in experiments with
corrupted or reduced datasets.

提出了一种新的基于 saliency-aware 的 mixup 方法 ——GuidedMixup，通过一种高效的配对算法来最小化配对图像的显著区域间的冲突，平滑地插值两幅图像以更好地保留显著区域，并且可以在分类数据集上提供良好的计算效率与泛化性能之间的平衡。

引导 Mixup：由显著图引导的高效 Mixup 策略

GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

We present a novel algorithm for text-driven image-to-image translation based
on a pretrained text-to-image diffusion model. Our method aims to generate a
target image by selectively editing the regions of interest in a source image,
defined by a modifying text, while preserving the remaining parts. In contrast
to existing techniques that solely rely on a target prompt, we introduce a new
score function, which considers both a source prompt and a source image,
tailored to address specific translation tasks. To this end, we derive the
conditional score function in a principled manner, decomposing it into a
standard score and a guiding term for target image generation. For the gradient
computation, we adopt a Gaussian distribution of the posterior distribution,
estimating its mean and variance without requiring additional training. In
addition, to enhance the conditional score guidance, we incorporate a simple
yet effective mixup method. This method combines two cross-attention maps
derived from the source and target latents, promoting the generation of the
target image by a desirable fusion of the original parts in the source image
and the edited regions aligned with the target prompt. Through comprehensive
experiments, we demonstrate that our approach achieves outstanding
image-to-image translation performance on various tasks.

本文提出了一种新颖的算法，利用预训练文本到图像扩散模型，通过在源图像中选择性地编辑被修改的文本定义下的感兴趣区域，生成目标图像，并引入新的评分函数，该函数同时考虑特定的翻译任务，实现了优秀的图像翻译表现。

基于条件得分指导的文本驱动图像到图像的翻译

Conditional Score Guidance for Text-Driven Image-to-Image Translation

Audio scene classification, the problem of predicting class labels of audio
scenes, has drawn lots of attention during the last several years. However, it
remains challenging and falls short of accuracy and efficiency. Recently,
Convolutional Neural Network (CNN)-based methods have achieved better
performance with comparison to the traditional methods. Nevertheless,
conventional single channel CNN may fail to consider the fact that additional
cues may be embedded in the multi-channel recordings. In this paper, we explore
the use of Multi-channel CNN for the classification task, which aims to extract
features from different channels in an end-to-end manner. We conduct the
evaluation compared with the conventional CNN and traditional Gaussian Mixture
Model-based methods. Moreover, to improve the classification accuracy further,
this paper explores the using of mixup method. In brief, mixup trains the
neural network on linear combinations of pairs of the representation of audio
scene examples and their labels. By employing the mixup approach for data
argumentation, the novel model can provide higher prediction accuracy and
robustness in contrast with previous models, while the generalization error can
also be reduced on the evaluation data.

本文研究了利用多通道卷积神经网络进行音频场景分类，通过 mixup 方法进行数据增强，相比传统方法有更高的精度和鲁棒性，并且可以在评估数据上减少泛化误差。