We introduce StableMaterials, a novel approach for generating photorealistic physical-based rendering (PBR) materials that integrate semi-supervised learning with Latent Diffusion Models (LDMs). Our method employs adversarial training to distill knowledge from existing large-scale image generation models, minimizing the reliance on annotated data and enhancing the diversity in generation. This distillation approach aligns the distribution of the generated materials with that of image textures from an SDXL model, enabling the generation of novel materials that are not present in the initial training dataset. Furthermore, we employ a diffusion-based refiner model to improve the visual quality of the samples and achieve high-resolution generation. Finally, we distill a latent consistency model for fast generation in just four steps and propose a new tileability technique that removes visual artifacts typically associated with fewer diffusion steps. We detail the architecture and training process of StableMaterials, the integration of semi-supervised training within existing LDM frameworks and show the advantages of our approach. Comparative evaluations with state-of-the-art methods show the effectiveness of StableMaterials, highlighting its potential applications in computer graphics and beyond. StableMaterials is publicly available at https://gvecchio.com/stablematerials.
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
Visual Intelligence Research Laboratories, NEC Corporation（NEC公司视觉智能研究实验室）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；RIKEN Center for AIP（理化学研究所AIP研究中心）
Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning. Unsupervised segmentation methods have shown promising results without requiring the time-consuming and laborious manual labeling process. In this paper, we present an approach for VS and cochlea segmentation in an unsupervised domain adaptation setting. Specifically, we first develop a cross-site cross-modality unpaired image translation strategy to enrich the diversity of the synthesized data. Then, we devise a rule-based offline augmentation technique to fur…
Imperial College London（帝国理工学院）；The Cyprus Institute（塞浦路斯研究所）；University of Athens（雅典大学）
A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. If not mitigated, bias leads to algorithms that exhibit unfair behaviour towards such groups. In this work, we address the problem of increasing the diversity of face datasets with respect to age. Concretely, we propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns by conditioning on multi-resolution age-discriminative representati…
University of Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；European Systems Integration（欧洲系统集成公司）
The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations. State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain and learn from these noisy pseudo labels. Recently introduced Mean Teacher Model is a promising way to mitigate the label noise. However, during the training, self-ensembled teacher-student networks quickly converge to a consensus which leads to a local minimum. We explore the possibility of using an asymmetric structure inside neural network to a…
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Zhejiang University of Technology（浙江工业大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；INSAIT, Sofia University（保加利亚索菲亚大学INSAIT研究院）
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Institute of High Performance Computing（高性能计算研究所）；XiDian University（西安电子科技大学）；National University of Singapore（新加坡国立大学）；Hubei University（湖北大学）
Seoul National University（首尔国立大学）；Sookmyung Women’s University（淑明女子大学）
Generative design refers to computational design methods that can automatically conduct design exploration under constraints defined by designers. Among many approaches, topology optimization-based generative designs aim to explore diverse topology designs, which cannot be represented by conventional parametric design approaches. Recently, data-driven topology optimization research has started to exploit artificial intelligence, such as deep learning or machine learning, to improve the capability of design exploration. This study proposes a reinforcement learning (RL) based generative design p…
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation
Department of Computer Science and Information Engineering, National Cheng Kung University（计算机科学与信息工程系，国立成功大学）；Department of Biomedical Informatics, Harvard Medical School, Harvard University（生物医学信息学系，哈佛医学院，哈佛大学）
Single-Teacher View Augmentation: Enhancing Knowledge Distillation with Student-Guided Perturbations
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所人机混合增强智能技术国家重点实验室）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）；Institute of Collaborative Innovation, University of Macau（澳门大学协同创新研究院）；Beijing Huahang Institute of Radio Measurement（北京华航无线电测量研究所）
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Nanyang Technological University（南洋理工大学）
Maximum Dispersion, Maximum Concentration: Enhancing the Quality of MOP Solutions
Computing Department, Universidade Federal de Ouro Preto（巴西欧鲁普里托联邦大学计算机系）；Federal Institute of Minas Gerais（巴西巴伊兰斯联邦理工学院）；School of Computer Science and Digital Technologies, Aston University（阿斯顿大学计算机科学与数字技术学院）
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Center for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Bytedance（字节跳动）；Department of Electrical and Computer Engineering, University of British Columbia（不列颠哥伦比亚大学电气与计算机工程学院）
