Demographic Influences on Contemporary Art with Unsupervised Style Embeddings

Style is a Distribution of Features

Neural style transfer (NST) is a powerful image generation technique that uses a convolutional neural network (CNN) to merge the content of one image with the style of another. Contemporary methods of NST use first or second order statistics of the CNN's features to achieve transfers with relatively little computational cost. However, these methods cannot fully extract the style from the CNN's features. We present a new algorithm for style transfer that fully extracts the style from the features by redefining the style loss as the Wasserstein distance between the distribution of features. Thus…

迈向负责任的人脸数据集：对解耦潜空间分布进行建模以从人口统计学群体中采样人脸图像

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

针对人脸识别数据集的人口统计学偏差问题，提出通过建模和采样StyleGAN解耦潜空间来生成平衡且可能无偏的合成人脸数据集，有效合成任意人口群体组合并开源代码。

Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice

Contemporary person re-identification (\reid) methods usually require access to data from the deployment camera network during training in order to perform well. This is because contemporary \reid{} models trained on one dataset do not generalise to other camera networks due to the domain-shift between datasets. This requirement is often the bottleneck for deploying \reid{} systems in practical security or commercial applications, as it may be impossible to collect this data in advance or prohibitively costly to annotate it. This paper alleviates this issue by proposing a simple baseline for d…

面向彩色到红外跨模态风格迁移的元学习

Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer

本文针对红外图像检测训练数据不足问题，提出元学习风格迁移（MLST），通过组合解析函数实现彩色到红外跨模态风格迁移，在多个红外数据集上取得最佳检测性能。

单细胞显微镜中的干预式风格迁移实现分布外泛化

Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy

针对单细胞显微镜图像，提出干预式风格迁移（IST）方法，通过生成干预式训练分布缓解虚假相关，显著提升分布外泛化，并发布代码与数据集。

现代生成模型复制艺术风格能力的批判性评估

A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles

本文批判性评估当代生成模型复制艺术风格的能力，基于新构建的AI-pastiche数据集和用户调查，从技术、美学等多维度分析其优势与局限，并探讨模型改进与创意应用机遇。

基于扩散的乌克兰手写文本生成与跨域风格迁移

Diffusion-Based Ukrainian Handwritten Text Generation with Cross-Domain Style Transfer

针对乌克兰语等非拉丁文字手写文本生成缺乏数据和模型泛化研究的问题，构建了乌克兰手写单词数据集并重新训练DiffusionPen模型，通过跨语言、零样本和少样本迁移实验验证了潜在扩散模型在跨域风格迁移中的有效性。

Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening

Contemporary methods have shown promising results on cardiac image segmentation, but merely in static learning, i.e., optimizing the network once for all, ignoring potential needs for model updating. In real-world scenarios, new data continues to be gathered from multiple institutions over time and new demands keep growing to pursue more satisfying performance. The desired model should incrementally learn from each incoming dataset and progressively update with improved functionality as time goes by. As the datasets sequentially delivered from multiple sites are normally heterogenous with doma…

理解动作捕捉行走数据集对老年群体的表征情况

Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets

本研究调研41个公开MoCap行走数据集，发现老年群体表征不足、年轻演员模拟的老年步态保真度低，提出定量评估方法并指出需完善该群体的数据集表征。

Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks

Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple differen…