Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream performance. To overcome this limitation, we propose SASSL: Style Augmentations for Self Supervised Learning, a novel data augmentation technique based on Neural Style Transfer. SASSL decouples semantic and stylistic attributes in images and applies transformations exclusively to their style while preserving content, generating diverse samples that better retain semantic information. SASSL boosts top-1 image classification accuracy on ImageNet by up to 2 percentage points compared to established self-supervised methods like MoCo, SimCLR, and BYOL, while achieving superior transfer learning performance across various datasets. Because SASSL can be performed asynchronously as part of the data augmentation pipeline, these performance impacts can be obtained with no change in pretraining throughput.
Digital Surgery（数字外科公司）；Kasetsart University（农业大学）
Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of catarac…
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
National University of Singapore（新加坡国立大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）；The University of Auckland（奥克兰大学）；The University of Melbourne（墨尔本大学）；The University of Tokyo（东京大学）
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）
StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
Westlake University（西湖大学）；Fudan University（复旦大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
An estimated 60% of smartphones sold in 2018 were equipped with multiple rear cameras, enabling a wide variety of 3D-enabled applications such as 3D Photos. The success of 3D Photo platforms (Facebook 3D Photo, Holopix, etc) depend on a steady influx of user generated content. These platforms must provide simple image manipulation tools to facilitate content creation, akin to traditional photo platforms. Artistic neural style transfer, propelled by recent advancements in GPU technology, is one such tool for enhancing traditional photos. However, naively extrapolating single-view neural style t…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）；Cornell University（康奈尔大学）
Inversion-Free Style Transfer with Dual Rectified Flows
Department of Computer Science and Technology, University of Science and Technology Beijing（北京科技大学计算机科学与技术系）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
South China University of Technology（华南理工大学）；Peng Cheng Laboratory（鹏城实验室）
Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexicon, is considerably expensive. Although data synthesis is a promising way to relieve data hunger, two key issues of handwriting synthesis, namely, style representation and content embedding, remain unsolved. To this end, we propose a novel method that can synthesize parameterized and controllable handwriting Styles for arbitrary-Length and Out-of-vocabulary text based on a Generative Adversarial Network (GAN), termed…
Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existing style transfer approaches on videos, i.e., transferring the style of a video frame by frame, requires an extremely large amount of computation resources. It is still technically unaffordable to perform style transfer of videos on mobile phones. To address this challenge, we pr…
Tune-Your-Style: Intensity-tunable 3D Style Transfer with Gaussian Splatting
School of Electronic and Computer Engineering, Peking University, Shenzhen, China（电子与计算机工程学院，北京大学深圳校区）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）；AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China（人工智能科学（AI4S）优选计划，北京大学深圳研究生院）；Department of Automation and BNRist, Tsinghua University, Beijing, China（自动化系和BNRist，清华大学，北京，中国）；Dalian University of Technology, China（大连理工大学）
SMCD: High Realism Motion Style Transfer via Mamba-based Diffusion
Academy for Engineering and Technology, Fudan University（工程与技术学院，复旦大学）；School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，华东师范大学）
University College London（伦敦大学学院）；Oxia Palus（Oxia Palus公司）；University of Cambridge（剑桥大学）
We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This coarse-to-fine generator architecture can increase the eff…
StylePrompter: Enhancing Domain Generalization with Test-Time Style Priors
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
