Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Guangdong Provincial Key Laboratory of Big Data Computing（广东省大数据计算重点实验室）
The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges, particularly due to the domain gap between labeled datasets from well-resourced centers and unlabeled datasets from less-resourced centers. This disparity affects the fairness of artificial intelligence algorithms in healthcare. We introduce Diffuse-UDA, a novel method leveraging diffusion models to tackle Unsupervised Domain Adaptation (UDA) in medical image segmentation. Diffuse-UDA generates high-quality image-mask pairs with target domain characteristics and various structures, thereby enhancing UDA tasks. Initially, pseudo labels for target domain samples are generated. Subsequently, a specially tailored diffusion model, incorporating deformable augmentations, is trained on image-label or image-pseudo-label pairs from both domains. Finally, source domain labels guide the diffusion model to generate image-label pairs for the target domain. Comprehensive evaluations on several benchmarks demonstrate that Diffuse-UDA outperforms leading UDA and semi-supervised strategies, achieving performance close to or even surpassing the theoretical upper bound of models trained directly on target domain data. Diffuse-UDA offers a pathway to advance the development and deployment of AI systems in medical imaging, addressing disparities between healthcare environments. This approach enables the exploration of innovative AI-driven diagnostic tools, improves outcomes, saves time, and reduces human error.
Zhejiang University（浙江大学）；Southern University of Science and Technology（南方科技大学）
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, unsupervised domain adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, large domain shifts and the sample scarcity in the target domain make existing UDA methods achieve suboptimal performance. To alleviate these issues, we propose a plug-and-play Diffusion-based Target Sampler (DTS) to generate high fidelity and diversity pseudo target samples. By introducing class-conditional information, the lab…
Tencent Jarvis Lab（腾讯贾维斯实验室）；National University of Defense Technology（国防科技大学）
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly. To tackle this problem, unsupervised domain adaptation (UDA) techniques are proposed to bridge the gap between different domains, for the purpose of improving model performance without annotation in the target domain. Particularly, UDA has a great value for multimodal medical image analysis, where annotation difficulty is a practical concern. However, most existing …
Information Sciences Institute（信息科学研究所）；University of Southern California（南加州大学）
A dominant approach for addressing unsupervised domain adaptation is to map data points for the source and the target domains into an embedding space which is modeled as the output-space of a shared deep encoder. The encoder is trained to make the embedding space domain-agnostic to make a source-trained classifier generalizable on the target domain. A secondary mechanism to improve UDA performance further is to make the source domain distribution more compact to improve model generalizability. We demonstrate that increasing the interclass margins in the embedding space can help to develop a UD…
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能中心（AIP））；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究院）
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation
Zhejiang University（浙江大学）；Southern University of Science and Technology（南方科技大学）；Hong Kong University of Science and Technology（香港科技大学）；Zhejiang Laboratory（之江实验室）
Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm
Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing（重庆生物感知与多模态智能信息处理重点实验室）；Chongqing University（重庆大学）；School of Microelectronics and Communication Engineering（微电子与通信工程学院）；University of Electronic Science and Technology of China（电子科学与技术大学）；Chongqing Key Laboratory of Image Cognition（重庆图像认知重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；School of Data Science（数据科学学院）；Lingnan University Hong Kong（香港岭南大学）；School of Science and Engineering（科学与工程学院）；Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（安全、可靠性与信任跨学科研究中心（SnT），卢森堡大学）；Cristal Laboratory, National School of Computer Sciences, University of Manouba（晶体实验室，国家计算机科学学校，曼努巴大学）
Institute of Optical and Electronics, Chinese Academy of Sciences（中国科学院光电技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Sheffield（谢菲尔德大学）
Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recognition is an under-explored problem, with lack of datasets and limited consideration of first-person video characteristics. This paper focuses on addressing this problem. Firstly, we propose two small-scale first-person video domain adaptation datasets: ADL$_{small}$ and GTEA-KITCHEN. Secondly, we introduce channel-temporal attention blocks to capture the channel-wise and temporal-wise relationships and model their inter…
Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation
College of Software, Jilin University（吉林大学软件学院）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, Jilin University（吉林大学知识驱动人机智能教育部工程研究中心）
University of Trento（特伦托大学）；Fondazione Bruno Kessler (FBK)（布鲁诺·凯斯勒基金会（FBK））
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have demonstrated excellent performances on many datasets, they often suffer from severe performance degradation when trained and tested on different datasets due to domain shift. In addition, as face images are considered highly sensitive data, the accessibility to large-scale datasets for model training is often denied. In this work, we tackle the above-mentioned problems by proposing the first Source-Free Unsupervised Dom…
Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a challenging task: to avoid a costly annotation of additional data, it aims at transferring knowledge from a domain with annotated data to a domain of interest with only unlabeled data. Pseudo-labeling approaches have proven to be effective for UDA re-ID. However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering. The lack of annotation in the domain of interest makes this choice non-trivial. Current approaches simply re…
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
Indian Institute of Information Technology Guwahati（印度古瓦哈蒂印度信息技术学院）；Hugging Face；National Yang Ming Chiao Tung University（国立阳明交通大学）；IBM Research（IBM研究院）
School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；Faculty of Geosciences and Engineering, Southwest Jiaotong University（西南交通大学地球科学与环境工程学院）；College of Land Science and Technology, China Agricultural University（中国农业大学土地科学与技术学院）；Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs（农业农村部农业遥感灾害重点实验室）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Supercomputing Center in Shenzhen（深圳国家超级计算中心）
Institute of Remote Sensing and Geographic Information System, Peking University（北京大学遥感与地理信息系统研究所）
The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods are facing two problems when performing RS images translation: 1) ignoring the scale discrepancy between two RS datasets wh…
PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering
School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Data Science, Fudan University（复旦大学数据科学学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
