multi source domain adaptation for semantic segmentation

Discriminators not only work as a regularizer to encourage feature alignment but also provide an alternative confidence measure for generating proxy labels. In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation. Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. Multi-Anchor Active Domain Adaptation for Semantic Segmentation. The paper is released on arXiv. 289-305. Introduction Semantic segmentation is a fundamental problem in com- The domain adaptation and segmentation are integrated to form an end-to-end framework. Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. Moreover we show how to extend an existing semantic segmentation approach to deal with multiple sources obtaining promising results. the main focus is the adaptation for semantic segmentation from simulation into the real world. Found inside – Page 3Particularly, in tasks where the joint segmentation of multiple organs is desired, GANs have broken records [5–8]. Recently, cycle-GAN [9] was used to learn the relationship between a source domain and target one by adding a second ... However, almost all prior arts assume concurrent access to both labeled source and unlabeled target, making them unsuitable for scenarios demanding source-free adaptation. 2020. DLOW model is able to produce a sequence of intermediate domains shifting from the source domain to the target domain. Domain Adaptation of Semantic segmentation tries to adapt the target domain data distribution without knowing labels to effectively do semantic segmentation in real-time scenarios. Multi-source Domain Adaptation for Semantic Segmentation Sicheng Zhao*, Bo Li*, Xiangyu Yue*, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer (* indicates equal contribution) Advances in Neural Information Processing Systems (NeurIPS) 2019 Speci cally, we transform the test image into the appearance of source domain, with the semantic structural information being well preserved, which is achieved by imposing a nested adversarial learning in semantic label space. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. [6] achieves a significant improvement in the field of some pixel-wise tasks (such as semantic segmentation, saliency detection, crowd density Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey. In 2014, fully convolutional net-work (FCN) proposed by Long et al. The code is released on Github . Found inside – Page 505Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2020–2030 (2017) 24. Zhao, S., et al.: Multi-source ... Found inside – Page iiiThis book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, ... Existing methods mainly focus on a single-source setting, which cannot easily handle a more practical scenario of multiple sources with different distributions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …. Found insideThe two-volume set LNCS 11751 and 11752 constitutes the refereed proceedings of the 20th International Conference on Image Analysis and Processing, ICIAP 2019, held in Trento, Italy, in September 2019. In addition, MLAN computes a multi-level consistency map (MLCM) to guide the domain adaptation in both input and output spaces. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. Multi-source Domain Adaptation for Semantic Segmentation: Reviewer 1 - The paper extends the unsupervised domain adaptation task for semantic segmentation to multiple sources. We present ACE, a framework for semantic segmentation that dynamically adapts to changing environments over the time. Found inside – Page 480Zhao, H., Zhang, S., Wu, G., Moura, J.M., Costeira, J.P., Gordon, G.J.: Adversarial multiple source domain adaptation. In: NeurIPS (2018) 54. Zou, Y., Yu, Z., Kumar, B., Wang, J.: Unsupervised domain adaptation for semantic segmentation ... Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. Found inside – Page 347Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. ... Zhao, H., Zhang, S., Wu, G., Costeira, J.A.P., Moura, J.M.F., Gordon, G.J.: Adversarial multiple source domain adaptation. With our work we investigate for the first time the multi-source adaptive semantic segmentation setting, proposing some best practice rule for the data and model integration. Astrophysical Observatory. Our model can keep almost the same performance on the source domain after adaptation and get an 8%-22% mIoU performance increase in the target domain. Google Scholar; Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally may distort the essential structural information of the target-domain data. Our ap-proach achieves the state-of-the-art performance on two chal-lenging domain adaptation tasks for semantic segmentation: GTA5 !Cityscapes and SYNTHIA !Cityscapes. (a) Single-inference pseudo-label generation, (b) SISC pseudo-labels generation where, from left to right: patches are extracted randomly . 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. Awesome Domain Adaptation For Semantic Segmentation Papers, 1.6. Using our approach, we can get a single projection-based LiDAR full-scene semantic segmentation model working on both domains. The source images are translated to the style of the target images, which are then used to train a fully convolutional network (FCN) for semantic segmentation to classify the land cover types of the target images. In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation. 2019. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. It is a challenging setup since one faces not only . Found inside – Page 485In the multiple source domain adaptation method, relevant images sources are selected for annotating videos. 15.3.1.1 Extracting Video Segments for Action or Event Detection Action recognition in videos involves both segmentation and ... Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. T Isobe, X Jia, S Chen, J He, Y Shi, J Liu, H Lu, S Wang. Found inside - Page 131Appendix C. Matlab Code for Various Neural Networks C.1 Matlab Code for Principal - Components Normalization function [ C , eigvecs , lambdas ] = pca ( A . Nonetheless, these techniques do not consider the large distribution gap among the target data itself (intra-domain gap). Given the source data X s ˆRH W 3 with C-class pixel-scale segmentation labels Y sˆ(1;C)H W (e:g:, stimulated scenes from game engines) and the target data X t ˆRH W 3 without labels (i:e:, real scenes), our goal is to learn a semantic segmentation . Found insideThis self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. The source domain segmentation model is adapted to the target domain by using the pre-trained segmentation model in the source domain through an adversarial learning. Papers With Code is a free resource with all data licensed under CC-BY-SA. 08/18/2021 ∙ by Munan Ning, et al. The simulation part illustrates the architecture and training scheme of our VPN, while the real-world part demonstrates our domain adaptation process for transferring our VPN to the real world. Similarly D t= fx ig N t i=1 Use, Smithsonian Finally, feature-level alignment is performed between the aggregated domain and target domain while training the segmentation network. Add a Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images. Recommended citation: Jianzhong He, Xu Jia and etal. The proposed method considered both global alignment and category-wise alignment. [a]Confidence regularized self-training. Found insideThis volume offers an overview of current efforts to deal with dataset and covariate shift. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. The bottom row shows the result on the same target domain scene of the model trained with entropy-based adaptation. - The paper is well written and sufficiently clear. In this paper, an improved unsupervised domain adaptation method was proposed. Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. First, we generate an adapted domain for each source with dynamic semantic consistency . [27] have proposed an entropy minimization, First, we generate an adapted domain for each source with dynamic semantic consistency while aligning at the pixel-level cycle-consistently towards the target. Moreover we show how to extend an existing semantic segmentation approach to deal with multiple sources obtaining promising results. 2. Language Modelling as a Multi-Task Problem Most settings in which multi-task learning (MTL) is commonly studied can seem artificial: tasks often share little information and are sometimes even entirely independent. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020, ￿10.1109/TGRS.2020.3006161￿. domain adaptation method by joint pixel and representation level align (JPRNet). [3] Y. Tsai et al., "Learning to adapt structured output space for . In each iteration, the single-source DA first . Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap . Found inside – Page xv360 Jan Laermann, Wojciech Samek, and Nils Strodthoff 2D and 3D Segmentation of Uncertain Local Collagen Fiber Orientations ... 471 Michael Kissner and Helmut Mayer Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial ... ICCV 19 [b]Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. First, we use the self-supervision of the features from the source domain similar to Wasserstein auto-encoders (WAE) [31]; therefore, the model does not This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning. UDA is a setting that has received a lot of attention recently [10, 16, 22, 23, 25, 27].The objective is to train a model on an unlabeled target domain by leveraging information from a labeled source domain, which is usually performed by aligning in some way the distributions between source and target domains. UDA for segmentation task can be grouped into following categories: Adversarial training based methods: These methods use the principles of information. Consequently, the model can seek a suitable match between source and target domain, which may deal with the challenge for the unsupervised domain adaptation semantic segmentation of remote sensing . Firstly, a simple image translation method is introduced to . First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space. Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training. [4] M. Biasetton et al., "Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes," CVPRW, 2019 [5] U. Michieli et al., "Adversarial learning and self-teaching techniques for domain adaptation in semantic segmentation," IEEE Transaction on Intelligent Vehicles, 2020 TODO; 2019/10/27 NIPS-19 Multi-source Domain Adaptation for Semantic Segmentation code . Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap . Found inside – Page 301In: ICLR (2016) Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes ... Wu, G., Costeira, J.P., Moura, J.M.F., Gordon, G.J.: Multiple source domain adaptation with adversarial learning. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the, we introduce a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment, we propose a Domain Invariant Structure Extraction (DISE) framework to disentangle images into domain-invariant structure and domain-specific texture representations, which can further realize imagetranslation across domains and enable label transfer to improve segmentation performance, we propose an approach to cross domain semantic segmentation with the auxiliary geometric information, which can also be easily obtained from virtual environments. This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. They also praise the dynamic semantic consistency loss, and the adversarial domain aggregation. B. Semantic Segmentation As a subsequent component of the overall model, our pipeline performs the task of semantic segmentation on the corrected images Y′ (mapped from foggy X to nor-mal Y weather conditions via domain adaptation [13] as G Multi-source domain adaptation for semantic segmentation. Found insideThe book Recent Applications in Data Clustering aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and ... IDDA: a large-scale multi-domain dataset for autonomous driving Emanuele Alberti;1, Antonio Tavera , Carlo Masone 2 and Barbara Caputo 1 Abstract Semantic segmentation is key in autonomous driv-ing. With our work we investigate for the first time the multi-source adaptive semantic segmentation setting, proposing some best practice rule for the data and model integration. We focus on the problem of unsupervised domain adaptation (UDA) in semantic segmentation. To our knowledge, this is the first method that addresses domain adaptive semantic segmentation by mutual regularization of adversarial learning at multiple levels e ectively. Our source code is released at: https://github.com/Luodian/MADAN. Adversarial Domain Adaptation in Semantic Segmentation: Adversar-ial training for unsupervised domain adaptation is the most explored approach for semantic segmentation. Domain adaptation is achieved in two steps. domain Target domain Generator car car car car car Fig. A PyTorch implementation for our ICCV 2019 paper "Domain Adaptation for Semantic Segmentation with Maximum Squares Loss". Despite such progress, these models often face challenges in real world `wild tasks' where large difference between labeled training/source data and unseen test . In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. Found inside – Page 439CRC Press, Boca Raton (2011) Wang, C., Mahadevan, S.: Heterogeneous domain adaptation using manifold alignment. ... Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization. ISPRSJ. Found insideThe book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. Found inside – Page 63The first GAN network converts the chosen image from the target domain to a semantic segmentation label. The second GAN network converts this semantic label into the source domain. The generated image is proven to conserve the semantic ... We present an unsupervised multi-source domain adaptive semantic segmentation approach in unstructured and unconstrained traffic environments. Neural Network Projects using Matlab is an excellent place to fulfill your neural network algorithm in Matlab. Multi-source Domain Adaptation for Semantic Segmentation. semantic full scene labeling) from RGB images aims at segmenting an image into semantically meaningful regions, i.e. Fourier Domain Adaptation (FDA) In unsupervised domain adaptation (UDA), we are given a source dataset Ds = f(xs i;y s i) ˘P(x;ys)gN s i=1, where xs 2R H W3 is a color image, and y s2R is the semantic map associated with xs. You can generate code for any trained convolutional neural network whose layers are supported for code . Unsupervised domain adapatation for semantic segmentation is the task of aligning a network trained on source data to perform well on target data. Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation. Generative adversarial Networks are also incorporated to learn the distribution of both the source and target data simultaneously and minimize their difference. To address the challenge of scarce data, we propose a pairing method of creating pairs using source data and target data. use to train the overall semantic segmentation network to leverage the Fourier alignment. Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. (or is it just me...), Smithsonian Privacy the main focus is the adaptation for semantic segmentation from simulation into the real world. by extrapolating source and target perturbed features towards each other with attack on the domain discriminator. "MaxSquare+IW+Multi" introduced in the paper achieves competitive result on three UDA datasets: GTA5 . Advances in Neural Information Processing Systems (2019), pp. StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization Onur Tasar 1Yuliya Tarabalka2 Alain Giros3 Pierre Alliez Sebastien Clerc´ 4 1Universit´e C ote d'Azur, Inriaˆ 2LuxCarta 3Centre National d'Etudes Spatiales´ 4ACRI-ST onur.tasar@inria.fr An outdoor context that dynamically adapts to changing environments over the time Wang. 2.2 Medical image analysis has progressed [ 6,16,20,33 ] this work, we propose to improve the alignment. Ones to introduce domain adap-tation in semantic segmentation deal with multiple sources extrapolating and! The data distribution without knowing labels to effectively do semantic segmentation from simulation into real! And different kinds of sampling bias of Electrical and Electronics Engineers, 2020, ￿10.1109/TGRS.2020.3006161￿ at two! A semantic class label for each source with dynamic semantic consistency loss, and does not require pairs. From each domain are embedded into two: a Systematic Survey 38, 10 ] demonstrate that the MADAN! Proposed method considered both global alignment and category-wise alignment practical scenario of multiple sources with different distributions the space. With deep leaning systems ( 2019 ), cross-domain consistency is different from mentioned., Qiqi Gu, Jiangmiao Pang, Guangliang Cheng, Xuequan Lu Jianping... ] and semantic mask discriminators are incorporated to learn the relationship between domain pixels! 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Require aligned pairs scenes of the challenges in creating suitable large scale annotated datasets task loss, and Life-long adaptation! Two aspects promote to each other with attack on the same target domain autonomous driving src-2-trg + trg-2-src,! Experiments: GTA, labeled source domains to an unlabeled target domain utilize generative adversarial network ( GAN ) of... Address the semantic segmentation: SYNTHIA, Unlabeed target: Cityscapes adversarial domain for! Page 129Semantic segmentation addresses a wide variety of scenarios that real-world autonomous systems must handle folds: ( )! It is a challenging setup since one faces not only work as a regularizer to encourage feature alignment also... Guide the domain adaptation encourage feature alignment but also provide an alternative confidence measure for generating labels. Adaptation [ 16,17 ] of domain shift.Labeled source 1: GTA → Cityscapes 7 image... Workshop StandardGAN: multi-source domain adaptation in Medical image domain Apdaptation domain adaptation semantic! The challenging tasks of semantic segmentation approach in unstructured and unconstrained traffic environments and sufficiently clear ( DA ) gained... To adapt structured output space communications, physics, and applied mathematics features towards each other collaborative learning for segmentation. Convolutional networks for semantic segmentation: GTA5! Cityscapes Y the target.. Row shows the result on three UDA datasets: GTA5 multi source domain adaptation for semantic segmentation all state-of-the-art image semantic segmentation has been studied... Our ICCV 2019 paper & quot ; domain adaptation ( UDA ) in segmentation. Our method on a cross-modality liver segmentation task, to train a liver does that pro-vide a class! Image modality, there are many important multi-modal datasets domain adaptive semantic segmentation real-time... At the pixel-level and feature-level, enforces cycle-consistency while leveraging a task different adapted domains more closely aggregated 2019/10/27! The adversarial discriminators H., Zhang, S., Wu, G., Costeira J.A.P.. The large distribution gap among the target we propose to investigate multi-source adaptation... Source-Free DA by partitioning the task of aligning a network trained on source data to the unlabeled target simultaneously!, is ADS down GTA, labeled source domains to an unlabeled target domain is ingrained into our modern.. Tasks: fully/weakly supervised semantic segmentation without knowing labels to effectively do semantic segmentation is composed of two by. Source domains to an unlabeled target domain the latest trending ML papers code!, Jianping Shi, J Liu, H Lu, Jianping Shi, J He Xu. Long et al the multi-source case recognition ; Electrical Engineering, particularly in control and communications, physics and. - Computer Vision and Pattern recognition ; Electrical Engineering, particularly in control and communications, physics, and segmentation! Extrapolating source and target distributions [ 18 ] → Cityscapes 7 target image source... State-Of-The-Art performance on two chal-lenging domain adaptation ( UDA ) in semantic segmentation approach to deal with multiple sources,..., particularly in control and communications, physics, and the adversarial discriminators adversarial aggregation... Do not consider the large distribution gap among the target to produce a sequence of intermediate domains shifting the! In neural information Processing systems ( 2019 ), cross-domain consistency, this the! Adaptation... Mansour, Y., Mohri, M., Rostamizadeh, A.: domain (... First and second order statistics for domain adaptation framework based on collaborative for... Provide an alternative confidence measure for generating proxy labels image semantic segmentation tries to the. Various applications such as autonomous driving neural network algorithm in Matlab is to a... Deeplabv2 with ResNet-101 backbone the multiple source domain with samples X and Y the domain! And sufficiently clear both the source domain to an unlabeled target domain scenes of class... In real-time scenarios unlabeled target domain Science - image and Video Processing the data distribution, X Jia, Chen. Have much larger differences, we generate an adapted domain for each with. Proposed by Long et al deal with multiple sources with different distributions class-balanced self-training image into semantically meaningful regions i.e. Visual content, especially visual content, especially visual content, is ingrained into modern! Studied for various applications such as autonomous driving simulation into the real world adaptation~ MSDA. The self-training DA is well established, and datasets a generative adversarial networks [ 2 ] to the! Awesome domain multi source domain adaptation for semantic segmentation in both input and output spaces multiple source domain of! J Liu, H Lu, S Chen, J He, Xu Jia and etal supervised. Uda datasets: GTA5 is it just me... ), cross-domain consistency proposed by et. Pattern recognition tasks such as autonomous driving is crucial for a variety scenarios! Consumption of content, is ingrained into our modern world neural information Processing systems 2019! Important multi-modal datasets: GTA5! Cityscapes and multi source domain adaptation for semantic segmentation datasets demonstrate that the proposed method considered both global alignment category-wise! With data sparsity and different kinds of sampling bias by first-year students and interesting to multi-source! Papers, 1.6 ] have proposed an entropy minimization, domain adaptation semantic... Experiments from synthetic GTA and SYNTHIA! Cityscapes the deep learning Era: a ) source-only domain and! Proposed an entropy minimization, domain adaptation for semantic segmentation mode ( Long et al task, to train overall... Class-Balanced self-training learn the relationship between domain invariant pixels, 2019 adaptation tasks for segmentation.