Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. However, models learned on synthetic images often do not generalize well to real images due to the domain shift. Our key idea to improve domain adaptation is to introduce a separate anchor task (such as facial landmarks) whose annotations can be obtained at no cost or are already available on both synthetic and real datasets. To further leverage the implicit relationship between the anchor and main tasks, we apply our \freeze technique that learns the cross-task guidance on the source domain with the final network layers, and use it on the target domain. We evaluate our methods on surface normal estimation on two pairs of datasets (indoor scenes and faces) with two kinds of anchor tasks (semantic segmentation and facial landmarks). We show that blindly applying domain adaptation or training the auxiliary task on only one domain may hurt performance, while using anchor tasks on both domains is better behaved. Our \freeze technique outperforms competing approaches, reaching performance in facial images on par with a recently popular surface normal estimation method using shape from shading domain knowledge.
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…
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally may distort the essential structural information of the target-domain data. To this end, we firstly propose to introduce a novel multi-anchor based active learning strategy to assist domain adaptation regarding the semantic segmentation task. By innovatively adopting multiple anchors instead o…
Graz University of Technology（格拉茨工业大学）；Christian Doppler Laboratory for Embedded Machine Learning（克里斯蒂安·多普勒嵌入式机器学习实验室）
LiDAR 3D object detection models are inevitably biased towards their training dataset. The detector clearly exhibits this bias when employed on a target dataset, particularly towards object sizes. However, object sizes vary heavily between domains due to, for instance, different labeling policies or geographical locations. State-of-the-art unsupervised domain adaptation approaches outsource methods to overcome the object size bias. Mainstream size adaptation approaches exploit target domain statistics, contradicting the original unsupervised assumption. Our novel unsupervised anchor calibratio…
Observation Quality in DSM-Assisted Aerial Localization: A Benchmark and Base-Anchored Uncertainty Calibration
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息研究院）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）
Nanjing University of Posts and Telecommunications（南京邮电大学）
Domain alignment (DA) has been widely used in unsupervised domain adaptation. Many existing DA methods assume that a low source risk, together with the alignment of distributions of source and target, means a low target risk. In this paper, we show that this does not always hold. We thus propose a novel metric-learning-assisted domain adaptation (MLA-DA) method, which employs a novel triplet loss for helping better feature alignment. We explore the relationship between the second largest probability of a target sample's prediction and its distance to the decision boundary. Based on the relatio…
In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capability of generalization to a different dataset or domain and requires post-processing (e.g. Non-Maximum Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the …
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Harvard Medical School（哈佛医学院）；Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学媒体网络协同创新中心）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Collaborative Innovation Center for Artificial Intelligence by MOE and Zhejiang Provincial Government (ZJU)（教育部与浙江省政府人工智能协同创新中心（浙江大学））；Zhejiang Lab（之江实验室）
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that tra…
RMIT University（皇家墨尔本理工大学）；The University of Melbourne（墨尔本大学）；Ford Motor Company（福特汽车公司）
Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. One approach to resolve this issue is to use the Unsupervised Domain Adaptation (UDA) technique that carries out knowledge transfer from a label-rich source domain to an unlabeled target domain. Outliers that exist in either source or target datasets can introduce additional challenges when using UDA in practice. In this paper, $α$-divergence is used as a measure to minimize the discrepancy between the source and target distributions while inheriting robustness…
Dalian University of Technology（大连理工大学）；Dalian Minzu University（大连民族大学）；Indiana University-Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challen…
Nanjing University of Aeronautics & Astronautics（南京航空航天大学）
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adap…
Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution. While extensive studies attested that deep learning models are vulnerable to adversarial attacks, the adversarial robustness of models in domain adaptation application has largely been overlooked. This paper points out that the inevitable domain distribution deviation in UDA is a critical barrier to model robustness on the target domain. To address the problem, we propose a novel Class-consistent Unsupervised Robust Domain Adapta…
Intuitive, Inc.（直观医疗公司）；Konica Minolta, Inc.（柯尼卡美能达公司）；University College London（伦敦大学学院）；University of Genoa（热那亚大学）；University of Burgundy（勃艮第大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications. However, many challenges exist around surgical data (and health data, more generally) needed to develop context-aware models. This work - presented as part of the Endoscopic Vision (EndoVis) challenge at the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020 conference - seeks to explore the potential for visual domain adaptation in surgery to overcome data privacy concerns. In particular, we propose to use video from virtual reality (VR) simulations of surgical exe…
University of Lincoln（林肯大学）；University of Aberdeen（阿伯丁大学）
Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of s…
High-Quality Pseudo-Label Generation Based on Visual Prompt Assisted Cloud Model Update
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Advanced Institute of Big Data（先进大数据研究院）
Universidade de São Paulo（圣保罗大学）；Mercado Libre（美客多）
Sketch-an-Anchor is a novel method to train state-of-the-art Zero-shot Sketch-based Image Retrieval (ZSSBIR) models in under an epoch. Most studies break down the problem of ZSSBIR into two parts: domain alignment between images and sketches, inherited from SBIR, and generalization to unseen data, inherent to the zero-shot protocol. We argue one of these problems can be considerably simplified and re-frame the ZSSBIR problem around the already-stellar yet underexplored Zero-shot Image-based Retrieval performance of off-the-shelf models. Our fast-converging model keeps the single-domain perform…
The University of Sydney（悉尼大学）；Tencent AI Lab（腾讯人工智能实验室）
Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. UDA is of particular significance since no extra effort is devoted to annotating target domain samples. However, the different data distributions in the two domains, or \emph{domain shift/discrepancy}, inevitably compromise the UDA performance. Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category…
South China University of Technology（华南理工大学）；Institute for Infocomm Research（资讯通信研究院）；Peng Cheng Laboratory（鹏城实验室）
Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available and instant inference on target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) prot…
I2R, A-STAR（A*STAR信息通信研究所）；South China University of Technology（华南理工大学）；Southwest Jiaotong University（西南交通大学）
Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT)…
A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image 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子工程学院）；Shanghai AI laboratory（上海人工智能实验室）
Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers
University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（北京工业光谱成像工程技术研究中心）；Nankai University（南开大学）；Tsinghua University（清华大学）
Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
Columbia University（哥伦比亚大学）；USC Information Sciences Institute（南加州大学信息科学研究所）
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to train a feature extractor is still a challenge. In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples. We propose a two-stage training scheme, Partner-Assisted Learning (PAL), which first trains a partner encoder to mo…
University of Chinese Academy of Sciences（中国科学院大学）；Kitware, Inc.（Kitware公司）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. In this paper, we propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning. To this end, we encode the dependencies across different granularity perspectives including pixel-, instance-, and category-levels simultaneously to align two domains. Based on pixel-level feature maps from the backbone network, we first develop the omni-scale gated fusion module to aggregate discriminative representations of instan…
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究院）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络科学与技术学院）
Zhejiang University（浙江大学）；Hikvision Research Institute（海康威视研究院）；National University of Singapore（新加坡国立大学）；Chongqing University（重庆大学）；Hainan Institute of Zhejiang University（浙江大学海南研究院）
Self-training for unsupervised domain adaptive object detection is a challenging task, of which the performance depends heavily on the quality of pseudo boxes. Despite the promising results, prior works have largely overlooked the uncertainty of pseudo boxes during self-training. In this paper, we present a simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. Specifically, we propose to leverage the uncertainty-g…
Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（电子、电气与通信工程学院，中国科学院大学）；School of Computer Science and Technology, University of Chinese Academy of Sciences（计算机科学与技术学院，中国科学院大学）；Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（智能信息处理重点实验室，计算技术研究所）
The Chinese University of Hong Kong（香港中文大学）；T stone Robotics Institute（T石头机器人研究所）；Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems（粤港澳人机协同智能系统联合实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a…
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）
Unsupervised domain adaptation (UDA) is an approach to minimizing domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images which improves the performance of the downstream tasks, e.g., cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this paper, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL), and dep…
