The core idea behind UDA is to minimize the divergence between the feature distributions of the source and target domains, which means to learn domain invariant features. Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Traditionally, unsupervised domain adaptation (UDA) [10, 20, 21, 29, 30, 33] is studied to tackle the domain-shift problem between the labeled source domain and unlabeled target domain for the classification task. 1: Weakly supervised domain adaptation approach for semantic segmentation in real urban scenes. Parameter Sensitivity Analysis. deeper than those used previously. Before the adaptation, we pre-train the network on the source domain for 70k, steps to get a high-quality source trained network. Semantic segmentation is a key problem for many computer vision tasks. Our maximum squares loss prevents the training process being We propose The general idea of UDA is utilizing the unlabeled data from the target domain to help minimize the performance gap between these two domains. As Table 2 shows, equipped with ResNet-101 backbone, our “MaxSquare+IW+Multi” method achieves state-of-the-art performance. While we mainly focus on image classification, we give pointers to papers that extend these ideas for other applications such as semantic segmentation, object detection, person re-identifications, and others. As argued in [28], it is important to adopt a stronger baseline model to understand the effect of different adaption approaches and enhance the performance for the practical application. Domain adaptation for semantic segmentation with maximum squares loss. We introduce a representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. A weakly-supervised domain adaptation network based on Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) and a built-up area detection module is proposed for satellite and aerial imagery.The GAN minimizes the distribution gap between source and target domain at output and latent . interpolation between these two numbers is more stable: where α is treated as a hyper-parameter to be selected by cross-validation. The distribution divergence can be measured by Maximum Mean Discrepancy (MMD) based methods [20, 21, 30] or adversarial learning based methods [10, 29]. Accuracy of different difficulty samples on A. While we focus our evaluation on object recognition tasks, the transform-based adaptation Random Resized Crop. Prior work considers transfer learning between attributes of the same domain. Our proposed adaptation method offers empirical performance which exceeds Verification of Maximum Square Loss. labeled examples in the new domain. Fine-tuning deep models in a new domain can require a significant 4 demonstrates, classes with higher accuracy always have more pixels on the label map, which leads to an imbalance in quantity. to transfer knowledge from a large labeled source domain to an unlabeled target domain.It contains a challenging simulation-to-real domain shift and consists of two tasks: (a) classification and (b) semantic segmentation. As a result, the networks are easier to train and deliver . previously published results on a standard benchmark visual domain adaptation a new CNN architecture which introduces an adaptation layer and an additional As OCDA is an emerging task initially appearing in CVPR2020, authors should give a full explanation about the OCDA setting to make the paper clear. Their method assumes the spatial priors are shared between source and target domains. Found inside – Page iiThe sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented ... There are five challenges: classification, detection, segmentation, action classification, and person layout. Found insideThis book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, in Macau, China, in April 2019. In the evaluation, we adopt the Intersection-over-Union (IoU) of each class and the mean-Intersection-over-Union (mIoU) as performance metrics. Thus the learning problem [.] . We adopt the model pre-trained on ImageNet, , except the final classifier layer. Besides, we Random Sized Crop. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. However, most existing works address the domain discrepancy by aligning the data distributions of two domains at a global image level whereas the local consistencies are largely neglected. categories with few or no target domain labels and moderate to large changes in the imaging conditions. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. Semantic Segmentation. experiments demonstrate the effectiveness of our proposed approach. ∙ export record. Figure 1: (Best viewed in color) The VisDA2017 challenge aims to test models ability to perform unsupervised domain adaptation, i.e. Following the evaluation protocol of other works [31, 36], we evaluate the IoU and mIoU of the shared 16 classes between two datasets and the 13 classes excluding the classes with ∗. The probability of different classes varies widely. Domain Adaptation for Semantic Segmentation With Maximum Squares Loss: Minghao Chen, Hongyang Xue, Deng Cai: link: 35: Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop: Nikos Kolotouros, Georgios Pavlakos, Michael J. Domain-Adversarial Loss. We show that our domain confusion metric can be used both to select the dimension of the adaptation layers, choose an . This work is a preprint of SYNASC'20 post-proceedings to be published by IEEE CPS. We apply the idea in weakly-supervised learning [34] to UDA and generate self-produced guidance to train the low-level feature. ∙ Meanwhile, we show optimizing our loss is equivalent to maximizing the Pearson χ2. Deep residual In this work the domain adaptation for semantic segmentation is studied and investigated thoroughly. ∙ We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. released at https://github. Found insideThis book covers the state-of-the-art methodology for mining time series databases. learning residual functions with reference to the layer inputs, instead of Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. After years of research, semantic segmentation models based on deep neural networks (, Deeplab [2, 3, 4], PSPNet [35]) can achieve astonishing performance on the real-world datasets, , PASCAL VOC-2012 [8], and Cityscapes [6]. , "Domain adaptation for semantic segmentation with maximum squares loss", In ICCV, 2019. In ICCV. 0 In this section, we present our major contributions, , the maximum squares loss, and the image-wise class-balanced weighting factor. Nevertheless, the performance heavily relies on high-quality labeled datasets, which need lots of manual effort. One possible way to reduce manual labeling cost is to adopt synthetic datasets constructed from the virtual world, , SYNTHIA [26] and GTA5 [25]. “MaxSquare+IW” surpasses “MaxSquare” method on the several small object classes, , traffic light, traffic sign, and motorbike. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. 2019. 0 The synthetic-to-real and cross-city adaption experiments show that our method can achieve state-of-the-art performance, without the discriminator in adversarial learning methods. The resulting augmented architecture can be trained using standard backpropagation, and can thus be implemented with little effort using any of the deep learning packages. Random Crop. In semi-supervised learning study, it is concluded that the information content of unlabeled examples decreases as classes overlap [1, 22]. Domain adaptation for semantic segmentation with maximum squares loss M Chen, H Xue, D Cai Proceedings of the IEEE/CVF International Conference on Computer Vision … , 2019 Found inside – Page iiThe three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, ... As for the class imbalance in the target domain, we propose to compute class weighting factor for each image, based on the prediction quantity of each class. Different distributions that the information content of unlabeled examples decreases as classes overlap [ 1, 22 ] achieves! On object recognition tasks, the maximum squares loss easier to train the low-level feature get a high-quality source network! On the source domain for 70k, steps to get a high-quality source trained network source target! 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