Despite growing interest in object detection, very few works address the extremely practical problem of cross-domain robustness especially for automative applications. In order to prevent drops in performance due to domain shift, we introduce an unsupervised domain adaptation method built on the foundation of faster-RCNN with two domain adaptation components addressing the shift at the instance and image levels respectively and apply a consistency regularization between them. We also introduce a family of adaptation layers that leverage the squeeze excitation mechanism called SE Adaptors to improve domain attention and thus improves performance without any prior requirement of knowledge of the new target domain. Finally, we incorporate a center loss in the instance and image level representations to improve the intra-class variance. We report all results with Cityscapes as our source domain and Foggy Cityscapes as the target domain outperforming previous baselines.
Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to …
Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we propose the Efficient Teacher framework for scalable and effective one-stage anchor-based SSOD training, consisting of Dense Detector, Pseudo Label Assigner, and Epoch Adaptor. Dense Detector is a baseline model that extends RetinaNet with dense sampling techniques inspired by YOL…
Cross-domain object detection is more challenging than object classification since multiple objects exist in an image and the location of each object is unknown in the unlabeled target domain. As a result, when we adapt features of different objects to enhance the transferability of the detector, the features of the foreground and the background are easy to be confused, which may hurt the discriminability of the detector. Besides, previous methods focused on category adaptation but ignored another important part for object detection, i.e., the adaptation on bounding box regression. To this end…
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation Models
Lanzhou University（兰州大学）；Shanghai Jiaotong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-conditional distributions by narrowing down cross-domain prototypes (class centers). Though great success,they ignore the significant within-class variance and the domain-mismatched semantics within the training batch, leading to a sub-optimal adaptation. To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the a…
Academia Sinica（中央研究院）；NEC Labs America（NEC美国实验室）；National Chiao Tung University（国立交通大学）；UC Merced（加州大学默塞德分校）；Google Research（谷歌研究院）
A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt feature alignment either on the image level or instance level. However, image-level alignment on global features may tangle foreground/background pixels at the same time, while instance-level alignment using proposals may suffer from the background noise. Different from existing solutions, we propose a domain adaptation framework that accounts for each pixel via predicting pixel-wise objectness and centerness. Specific…
Tencent Youtu Lab（腾讯优图实验室）；The Chinese University of Hong Kong（香港中文大学）
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easil…
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The University of Hong Kong（香港大学）；Tencent（腾讯）
To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific properties. However, domain adaptation tasks are always addressed in two interactive aspects: domain transfer and the enhancement of discrimination, which requires the selected data to be both uncertain under the model and diverse in feature space. Contrary to active learning in classification tasks, it is usually challenging to select pixels that contain both the above properties in segmentation tasks, leading to the c…
Strong image search models can be learned for a specific domain, ie. set of labels, provided that some labeled images of that domain are available. A practical visual search model, however, should be versatile enough to solve multiple retrieval tasks simultaneously, even if those cover very different specialized domains. Additionally, it should be able to benefit from even unlabeled images from these various retrieval tasks. This is the more practical scenario that we consider in this paper. We address it with the proposed Grappa, an approach that starts from a strong pretrained model, and ada…
Wuhan University（武汉大学）；NC State University（北卡罗来纳州立大学）
This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolu…
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
Domain adaptation (DA) or domain generalization (DG) for face presentation attack detection (PAD) has attracted attention recently with its robustness against unseen attack scenarios. Existing DA/DG-based PAD methods, however, have not yet fully explored the domain-specific style information that can provide knowledge regarding attack styles (e.g., materials, background, illumination and resolution). In this paper, we introduce a novel Style-Guided Domain Adaptation (SGDA) framework for inference-time adaptive PAD. Specifically, Style-Selective Normalization (SSN) is proposed to explore the do…
Nanjing University of Science and Technology（南京理工大学）
Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac structure segmentation if training cases and testing cases are from the same distribution. However, the performance would be degraded if the testing cases are from a distinct domain (e.g., new MRI scanners, clinical centers). In this paper, we propose a histogram matching (HM) data augmentation method to eliminate the domain gap. Specifically, our method generates new training cases by using HM to transfer the intensity distribution of testing cases to existing training cases. The proposed method is quite simple and c…
DA-SSL: self-supervised domain adaptor to leverage foundational models in turbt histopathology slides
National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心）；University of California, San Francisco（加州大学旧金山分校）；Yale University（耶鲁大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Washington（华盛顿大学）
The Chinese University of Hong Kong（香港中文大学）；ByteDance Inc.（字节跳动）；Hong Kong University of Science and Technology（香港科技大学）；Microsoft Research Asia（微软亚洲研究院）
This work aims at transferring a Generative Adversarial Network (GAN) pre-trained on one image domain to a new domain referring to as few as just one target image. The main challenge is that, under limited supervision, it is extremely difficult to synthesize photo-realistic and highly diverse images, while acquiring representative characters of the target. Different from existing approaches that adopt the vanilla fine-tuning strategy, we import two lightweight modules to the generator and the discriminator respectively. Concretely, we introduce an attribute adaptor into the generator yet freez…
National Institute on Aging（美国国家衰老研究所）；The Johns Hopkins University（约翰斯·霍普金斯大学）；National Institute of Health（美国国立卫生研究院）
Domain shift is a major problem for deploying deep networks in clinical practice. Network performance drops significantly with (target) images obtained differently than its (source) training data. Due to a lack of target label data, most work has focused on unsupervised domain adaptation (UDA). Current UDA methods need both source and target data to train models which perform image translation (harmonization) or learn domain-invariant features. However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or…
Monash University（莫纳什大学）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；University of Melbourne（墨尔本大学）
Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will enable CVD risk estimation in various scenarios and improve healthcare democratization. However, there are still significant issues to be solved. One of the top priority issues is the different camera differences between the databases for research material and the samples in the production environment. Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a …
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；IHU Strasbourg（斯特拉斯堡大学医院研究所）
The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better understand the clinical activities and the spatial layout of the OR. This is challenging, not only because OR images are very different from traditional vision datasets, but also because data and annotations are hard to collect and generate in the OR due to privacy concerns. To address these concerns, we first study how joint person pose estimatio…
Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks. In this study, we address category-level full 6D object pose estimation in the context of depth modality, introducing a novel part-based architecture that can tackle the above-mentioned challenges. Our architecture particularly adapts the distribution shifts arising from shape discrepancies, and naturally removes the variations of texture, illumination, pose, etc., so we call it as "Intrinsic Structure Adaptor (ISA)". We engineer ISA based on the followings: i) "Semanti…
University of Florida（佛罗里达大学）；UNC Charlotte（北卡罗来纳大学夏洛特分校）
The diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a multi-scale input along with multiple domain adaptors applied hierarchically in both feature and output spaces. The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge. Multi-scale inputs can reduce the information loss due to the poolin…
Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology（Chemnitz工业大学电气工程与信息技术学院）
Synthetic images are one of the most promising solutions to avoid high costs associated with generating annotated datasets to train supervised convolutional neural networks (CNN). However, to allow networks to generalize knowledge from synthetic to real images, domain adaptation methods are necessary. This paper implements unsupervised domain adaptation (UDA) methods on an anchorless object detector. Given their good performance, anchorless detectors are increasingly attracting attention in the field of object detection. While their results are comparable to the well-established anchor-based m…
Indiana University-Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）；University of Massachusetts Dartmouth（马萨诸塞大学达特茅斯分校）
Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better fight off the cross-domain distribution divergences. However, it is essential to align target data with only a small set of source data. In this paper, we develop a novel Discriminative Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target la…
Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Biomedical Image Analysis Group, Imperial College London（帝国理工学院生物医学图像分析组）；Comprehensive Cancer Imaging Centre, Hammersmith Hospital, Imperial College London（帝国理工学院哈默史密斯医院综合癌症成像中心）；The Royal Marsden NHS Foundation Trust, London（伦敦皇家马斯登国民保健服务基金会信托）
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentation, performance often degrades when algorithms are applied on new data acquired from different scanners or sequences than the training data. Manual annotation is costly and time consuming if it has to be carried out for every new target domain. In this work, we investigate automatic selection of suitable subjects to be annotated for supervised domain adaptation using the concept of reverse classification accuracy (RCA…
Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single…
AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models
Carnegie Mellon University（卡内基梅隆大学）；Brown University（布朗大学）；National Institute of Technology Karnataka（印度卡纳塔克国家理工学院）；University of Pittsburgh（匹兹堡大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Microsoft Research（微软研究院）；Alibaba DAMO Academy（阿里巴巴达摩院）；Hefei University of Technology（合肥工业大学）；Zhejiang University（浙江大学）
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification. However, negative transfer may deteriorate the performance due to the feature distribution divergence between two datasets and task semantic difference in diagnosing pneumonia and COVID-19 that rely on different characteristics. It is even more challenging when the target dataset has no labels available, i.e., unsupervised task transfer…
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Electronic Science and Technology of China（电子科技大学）；West China Hospital, Sichuan University（四川大学华西医院）；Chengdu First People’s Hospital（成都市第一人民医院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation
HUMAN-tech；Universidad Politécnica de Valencia (UPV)（瓦伦西亚理工大学）；Valencian Graduate School and Research Network for Artificial Intelligence (valgrAI)（瓦伦西亚人工智能研究生学院及研究网络）
Radboud University Medical Center（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Nanjing University of Information Science and Technology（南京信息工程大学）
Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, and we also utilize proxy tasks of VS segmentation and brain parcellation to restrict the consistency of image structures in domain adaptation. After …
Indiana University-Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）
Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain. Most conventional domain adaptation (DA) efforts concentrate on learning domain-invariant features to mitigate the distribution disparity across domains. However, it is crucial to alleviate the negative influence caused by the irrelevant source domain categories explicitly for PDA. In this work, we propose an Adaptively-Accumulated Knowledge Transfer framework (A$^2$KT) to align the relevant categories across two dom…
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）
Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. Most existing methods assign pseudo-labels to the target data by generating feature prototypes. However, due to the discrepancy in the data distribution between the source domain and the target domain and category imbalance in the target domain, there are severe class biases in the generated feature prototypes and noisy pseudo-labels. Besides, the data structure of the target domain is often ignored, which is crucial …
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；School of Mathematical Sciences, Harbin Engineering University（哈尔滨工程大学数学科学学院）；Shanghai AI Lab（上海人工智能实验室）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）
University of Electronic Science and Technology of China（电子科技大学）；Sichuan University（四川大学）；The University of Queensland（昆士兰大学）
Recent works in domain adaptation always learn domain invariant features to mitigate the gap between the source and target domains by adversarial methods. The category information are not sufficiently used which causes the learned domain invariant features are not enough discriminative. We propose a new domain adaptation method based on prototype construction which likes capturing data cluster centers. Specifically, it consists of two parts: disentanglement and reconstruction. First, the domain specific features and domain invariant features are disentangled from the original features. At the …
