Deep Learning models are incredibly data-hungry and require very large
labeled datasets for supervised learning. As a consequence, these models often
suffer from overfitting, limiting their ability to generalize to real-world
examples. Recent advancements in diffusion models have enabled the generation
of photorealistic images based on textual inputs. Leveraging the substantial
datasets used to train these diffusion models, we propose a technique to
utilize generated images to augment existing datasets. This paper explores
various strategies for effective data augmentation to improve the out-of-domain
generalization capabilities of deep learning models.

通过利用生成的图像来增强现有数据集的技术，该论文探讨了各种有效的数据增强策略，以提高深度学习模型的跨域泛化能力。

利用语言进行图像的语义增强

Semantic Augmentation in Images using Language

Deep learning, based on which many modern algorithms operate, is well known
to be data-hungry. In particular, the datasets appropriate for the intended
application are difficult to obtain. To cope with this situation, we introduce
a new dataset called ETRI-Activity3D, focusing on the daily activities of the
elderly in robot-view. The major characteristics of the new dataset are as
follows: 1) practical action categories that are selected from the close
observation of the daily lives of the elderly; 2) realistic data collection,
which reflects the robot's working environment and service situations; and 3) a
large-scale dataset that overcomes the limitations of the current 3D activity
analysis benchmark datasets. The proposed dataset contains 112,620 samples
including RGB videos, depth maps, and skeleton sequences. During the data
acquisition, 100 subjects were asked to perform 55 daily activities.
Additionally, we propose a novel network called four-stream adaptive CNN
(FSA-CNN). The proposed FSA-CNN has three main properties: robustness to
spatio-temporal variations, input-adaptive activation function, and extension
of the conventional two-stream approach. In the experiment section, we
confirmed the superiority of the proposed FSA-CNN using NTU RGB+D and
ETRI-Activity3D. Further, the domain difference between both groups of age was
verified experimentally. Finally, the extension of FSA-CNN to deal with the
multimodal data was investigated.

介绍了一个名为 ETRI-Activity3D 的新数据集，关注老年人在机器人监视下的日常活动，同时提出一种名为 FSA-CNN 的新型网络用于处理多模态数据，并在实验证明了其在 NTU RGB+D 和 ETRI-Activity3D 上的优越性。