This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA framework that incorporates multiple mutually complementary domain alignment techniques. ECACL includes two categorical domain alignment techniques that achieve class-level alignment, a strong data augmentation based technique that enhances the model's generalizability and a consistency learning based technique that forces the model to be robust with image perturbations. These techniques are applied on one or multiple of the three inputs (labeled source, unlabeled target, and labeled target) and align the domains from different perspectives. ECACL unifies them together and achieves fairly comprehensive domain alignments that are much better than the existing methods: For example, ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at \url{https://github.com/kailigo/pacl}.
Human skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. This is because skin regions appear to be relatively uniform and many argue that there is a small chromatic variation among different samples. However, we found that there are strong biases in the datasets commonly used to train or tune skin detection methods. Furthermore, the lack of contextual information may hinder the performance of local approaches. In this paper we present a comprehensive evaluation of holistic and local …
Due to the large cross-modality discrepancy between 2D sketches and 3D shapes, retrieving 3D shapes by sketches is a significantly challenging task. To address this problem, we propose a novel framework to learn a discriminative deep cross-modality adaptation model in this paper. Specifically, we first separately adopt two metric networks, following two deep convolutional neural networks (CNNs), to learn modality-specific discriminative features based on an importance-aware metric learning method. Subsequently, we explicitly introduce a cross-modality transformation network to compensate for t…
Data inconsistency and bias are inevitable among different facial expression recognition (FER) datasets due to subjective annotating process and different collecting conditions. Recent works resort to adversarial mechanisms that learn domain-invariant features to mitigate domain shift. However, most of these works focus on holistic feature adaptation, and they ignore local features that are more transferable across different datasets. Moreover, local features carry more detailed and discriminative content for expression recognition, and thus integrating local features may enable fine-grained a…
Dalian University of Technology（大连理工大学）；Data Storage and Intelligent Vision Technical Research Dept Huawei Cloud（华为云数据存储与智能视觉技术研究部）；Key Lab of Machine Perception Peking University（北京大学机器感知重点实验室）
Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation especially for dense pixel prediction tasks such as semantic segmentation. Recently, both unsupervised domain adaptation (UDA) from large amounts of synthetic data and semi-supervised learning (SSL) with small set of labeled data have been studied to alleviate this issue. However, there is still a large gap on performance compared to their supervised counterparts. We focus on a more practical setting of semi-supervised do…
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
South China University of Technology（华南理工大学）；University of Amsterdam（阿姆斯特丹大学）；Hong Kong University of Science and Technology（香港科技大学）；Singapore Management University（新加坡管理大学）
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院多光谱信息智能处理技术国家重点实验室）；Huawei International Co. Ltd.（华为国际有限公司）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Electrical Engineering and Computer Science, Peking University（北京大学电气与计算机工程学院）
Guangdong University of Technology（广东工业大学）；Sun Yat-Sen University（中山大学）；The Hong Kong Polytechnic University（香港理工大学）
To address the problem of data inconsistencies among different facial expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs) have been extensively devised in recent years. Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors. In this work, we first analyze the performance effect caused by these inconsistent choices, and then re-implement some well-performing CD-FER and recently published domain adaptation algorithms. We ensure that all these algorithms ad…
Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification
State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipments, Xidian University, Xi’an, Shaanxi 710071, China（高性能电子装备机电一体化制造国家重点实验室，西安电子科技大学，陕西西安710071，中国）；Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China（复杂系统中心，机电工程学院，西安电子科技大学，陕西西安710071，中国）
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony
Wolf 1069B Lab, Sany Group（沃尔夫1069B实验室，三一集团）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Engineering and Technology, University of Washington, Tacoma（华盛顿大学塔科姆分校工程与科技学院）；Alibaba Group（阿里巴巴集团）；Department of Engineering, King’s College London（伦敦大学国王学院工程系）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
EventFly: Event Camera Perception from Ground to the Sky
National University of Singapore（新加坡国立大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Institute for Infocomm Research, A*STAR（新加坡科研局信息通信研究所）；IPAL, CNRS IRL 2955（法国国家科学研究中心IPAL国际研究实验室）；CerCo, CNRS UMR 5549, Université Toulouse III（法国国家科学研究中心CerCo实验室，图卢兹第三大学）
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）
In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ aims at reducing noise in pseudo label generation as well as alleviating the negative impacts of noisy pseudo labels on model training. First, ST3D++ pre-trains the 3D object detector on the labeled source domain with random object scaling (ROS) which is designed to reduce target domain pseudo label noise arising from object scale bias of the source domain. Then, the detector is progressively improved through alternati…
Sapienza University of Rome（罗马大学）；University of Tübingen（蒂宾根大学）；University of Trento（特伦托大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Politecnico di Torino（都灵理工大学）
Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core challenge of Zero-Shot Learning (ZSL) is to cope with the semantic-shift whereas the main challenge of Domain Adaptation and Domain Generalization (DG) is the domain-shift. While historically ZSL and DG tasks are tackled in isolation, this work develops with the ambitious goal of solving them jointly,i.e. by recognizing unseen visual concepts in unseen domains. We presentCuMix (CurriculumMixup for recognizing unseen…
We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. However, aligning the marginal feature distributions does not guarantee the alignment of class conditional distributions. This limitation is more evident when adapting object detectors as the domain discrepancy is larger compared to the image classification task, e.g. various number of …
Beijing Institute of Technology（北京理工大学）；Inceptio Technology（嬴彻科技）
Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works suggest that performing category alignment can alleviate domain shift reasonably. However, they are mainly based on image-to-image adversarial training and little consideration is given to semantic variations of an object among images, failing to capture a comprehensive picture of different categories. This motivates us to explore a holistic representative, the semantic distribution from each category in source domain, t…
Uncertainty-quantified Rollout Policy Adaptation for Unlabelled Cross-domain Temporal Grounding
Queen Mary University of London（伦敦女王学院）；Hong Kong University of Science and Technology（香港科学与技术大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；University College London（伦敦大学学院）
Sun Yat-Sen University（中山大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-supervised pre-training methods have achieved impressive performance on various vision tasks under the prevailing pretrain-finetune paradigm, their generalization capacity to multi-task learning scenarios is yet to be explored. In this paper, we extensively investigate the transfer performance of various types of self-supervised methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic segmentation,…
ETH Zürich（苏黎世联邦理工学院）；JD AI Research（京东人工智能研究院）；Peking University（北京大学）
Despite great progress in supervised semantic segmentation,a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics acros…
Tackle CSM in JPEG Steganalysis with Data Adaptation
LABEL4.AI；Univ. Lille（里尔大学）；CNRS（国家科学研究中心）；Centrale Lille（里尔中央理工大学）；UMR 9189 CRIStAL（里尔大学UMR 9189 CRIStAL）；IMT Nord Europe（北欧IMT）；Centre for Digital System（数字系统中心）；Department of Computers（计算机系）
Beihang University（北京航空航天大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）；SenseTime Research（商汤科技研究院）；Shanghai AI Laboratory（上海人工智能实验室）
Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled sou…
HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学国家机器人视觉感知与控制工程研究中心）；State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University（浙江大学极端光子学与仪器科学国家重点实验室）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Hunan Vanguard Group Corporation Limited（湖南先锋集团有限公司）
