adversarial pyramid network for video domain generalization

adversarial pyramid network for video domain generalization

2020年8月24日から27日まで開催されていました2020 European Conference on Computer Vision (ECCV 2020)は、画像解析分野におけるヨーロッパのトップカンファレンスです。ECCV 2020に採択された論文と参考資料に一覧です。 Volume Edited by: Tal Arbel Ismail Ben Ayed Marleen de Bruijne Maxime Descoteaux Herve Lombaert Christopher Pal Series Editors: Neil D. Lawrence Mark Reid Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. 0 ... domain 85. generative 84. networks 80. architecture 74. input 68. tensorflow 64. output 64. layers 61. generating 59. spark 56. Firstly, we design a representation-guided adversarial framework to vividly transfer selected caricature style with unsupervised learning. Domain Generalization With Adversarial Feature Learning Haoliang Li, Sinno Jialin Pan, Shiqi Wang, Alex C. Kot Pyramid Stereo Matching Network Jia-Ren Chang, Yong-Sheng Chen Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars Ana I. Maqueda, Antonio Loquercio, Guillermo Gallego, Narciso García, Davide Scaramuzza AAAI Conference on Artificial Intelligence (AAAI) 2020. I have tried to collect and curate some publications form Arxiv that related to the generative adversarial networks, and the results were listed here. [21] proposed a generative model named Generative Adversarial Networks (GAN). • Capable of inferring photo-realistic natural images for 4 upscaling factors. video game characters and 3D cars. Today, we want to look into … derstanding the Role of Individual Units in a Deep Neural Network." Finally, we empirically show the robustness and versatility of our approach in two defence scenarios Spoiler Alert! The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. remote sensing Article Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery Mohamad M. Al Rahhal 1, Yakoub Bazi 2,* , Taghreed Abdullah 3, Mohamed L. Mekhalfi 4, Haikel AlHichri 2 and Mansour Zuair 2 1 Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia; … The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. Title: Adversarial Pyramid Network for Video Domain Generalization. solve any complex real-world problem. Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification D. Xie, C. Deng , H. Wang, C. Li, and D. Tao. Temporal Pyramid Network for Action Recognition Fangqiu Yi Dec. 4, ... Recurrent-In-Recurrent Network for Video Quality Assessment Yingjian Song Nov. 27, ... Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses Yujia Liu Feb. 14, 2020. SELF-BALANCED LEARNING FOR DOMAIN GENERALIZATION: 1166: Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction: 1539: SELF-GUIDED ADVERSARIAL LEARNING FOR DOMAIN ADAPTIVE SEMANTIC SEGMENTATION: 2769: SELF-ORGANIZED RESIDUAL BLOCKS FOR IMAGE SUPER-RESOLUTION: 3190 Saed: 1. I am an Assistant Professor at Dept. ∙ Tsinghua University ∙ 0 ∙ share . Pyramid module: The Gauss‐Laplacian pyramid is introduced into our network. adult image classification by a local-context aware network: 1795: adversarial domain adaptation with a domain similarity discriminator for semantic segmentation of urban areas: ... domain generalization through source-specific nets: 1302: ... temporal pyramid relation network for video … Or, feature-level adaptation aligns intermediate network features between the domains . Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. [code] Xiaopeng Zhang, Yang Yang, Jiashi Feng. With popularity of consumer electronics in our daily life, this topic has become more and more attractive. arXiv preprint arXiv:1412.4446 (2014). While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). , 2017a ). Yeh R A, Chen C, Lim T Y, et al (2016). Domain adaptive faster r-cnn for object detection in the wild. This is a natural extension to the previous topic on variational autoencoders (found here).We will see that GANs are largely superior to variational autoencoders, but are notoriously difficult to work with. [9] employed a multi-level adversarial network to perform output space domain adaptation at different feature levels. AdversarialNAS: Adversarial Neural Architecture Search for GANs This paper introduces a new research problem of video domain generalization (video DG) where most state-of-the-art action recognition networks degenerate due to the lack of exposure to the target domains of divergent distributions. While content specifies which objects are in the video, motion describes their dynamics. MAIN CONFERENCE CVPR 2019 Awards. Domain Generalization via Model-Agnostic Learning of Semantic Features; EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks; AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering; A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern ICCV 2017 Workshop on TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision, pp.2623-2629, 2017. This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.Feel free to make a pull request to contribute to this list. A latent encoder (or a mapping network) that produces a style code for each domain, one of which is randomly selected during training. 13. Adversarial Pyramid Network for Video Domain Generalization. Facebook researchers will also be organizing and participating in virtual tutorials and workshops throughout the week. 1682: Self domain adapted network 2127: Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI 394: Dual-task Self-supervision for Cross-Modality Domain Adaptation 511: Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation 158: ... Adversarial Pyramid Network for Video Domain Generalization. Domain Adversarial Neural Network: 1. Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification with K-Means Features The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. ; If a paper is added to the list, another paper (usually from *More Papers from 2016" section) should … Zhaiming & Jianwei 2. SOURCE. 4. "Generative Adversarial Networks".GAN arXiv code. directional feature pyramid network (BiFPN)—primarily for the FAS problem—in an effort to extract multi-scaled features while also coupled with the EfficientNet [21] feature extractor. Afternoon 1. Such a distribution mismatch may lead to a significant performance drop. Facebook AI Research is also organizing a tutorial on Visual Recognition for Images, Video, and 3D to … [code] Li Yuan, Ping Li, Li Zhou, Francis Tay, Jiashi Feng. 0 comments . Generative Adversarial Network Definition. A list of top 100 deep learning papers published from 2012 to 2016 is suggested. Cross-Domain Semantic Segmentation of Urban Scenes Via Multi-Level Feature Alignment. The framework is a two-player game that the generator is trained to generate images from inputed noises to fool the discriminator while the discriminator is trained to well discriminate real samples and fake samples. Shape inpainting using 3D generative adversarial network and recurrent convolutional networks. An Exemplar-based Multi-view Domain Generalization Framework for Visual Recognition. However, the emergence of a large number of vehicles poses the critical but challenging problem of vehicle re-identification (reID). Adversarial invariant feature learning with accuracy constraint for domain generalization. The complexity of the task arises from the commonly-agreed definition of an abnormal event, that is, a rarely occurring event that typically depends on the surrounding context. (2015) used a Laplacian pyramid of adversarial generator and dis-criminators to synthesize images at … Our model consists of a recurrent generator network G, which is a deterministic video prediction model that maps an initial image x 0 and a sequence of latent random codes z 0: T − 1, to the predicted sequence of future images ^ x 1: T. Intuitively, the latent codes encapsulate any ambiguous or stochastic events that might affect the future. Title: Adversarial Pyramid Network for Video Domain Generalization. The proposed method can adapt to the target domain (i.e. Domain Generalization With Adversarial Feature Learning . Auto-TLDR; Cross-Domain Semantic Segmentation Using Generative Adversarial Networks Given enough number of hidden layers of the neuron, a deep neural network can approximate i.e. ‪Shanghai Jiao Tong University‬ - ‪‪664 citazioni‬‬ - ‪Deep Learning‬ ... We propose the first Multi-target Adversarial Network (MAN), ... We would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. 10015-10023 Focus Is All You Need: Loss Functions for Event-Based Vision pp. In this paper, we argue that the curse of dimensionality is the underlying reason of limiting the performance of state-of-the-art algorithms. Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection pp. generative adversarial network to generate realistic images based on a Laplacian pyramid framework (LAPGAN). [40] proposed an unsupervised domain adaptation strategy, which uses adversarial learning M. ture [8, 9, 10], or use pyramid structure [11], for the purpose of enabling the network to have varying receptive elds, so as to e ectively deal with di erent degrees of blur. [13] extended the above by introducing a robust loss formulation and making architectural modifications for im-proving speed and accuracy. Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder. DenseRaC : Joint 3D Pose and Shape Estimation by Dense Render-and-Compare, Yuanlu Xu 16. arXiv preprint arXiv:1912.03716 ... Probabilistic Video Prediction from Noisy Data with a Posterior Confidence. The dose distribution results of real CT and pseudo CT images obtained based on the SGAN method are shown in Figure 11 , respectively. This is the second part of a 3 part tutorial on creating deep generative models specifically using generative adversarial networks. sGAN, stacked generative adversarial network; cGAN, conditional generative adversarial network. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. arXiv preprint arXiv:1609.04802. In 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). View Indenpendent Generative Adversarial Network for Novel View Synthesis, Xiaogang Xu. Our method maps the video frames into a low-dimensional feature space using the class-discriminative spatial attention map for CNNs. Images from a source domain can be modified at the pixel-level to resemble a target domain . 12/08/2019 ∙ by Zhiyu Yao, et al. 4391-4400. [full paper] Sicheng Zhao, Chuang Lin, Pengfei Xu, Sendong Zhao, Yuchen Guo, Ravi Krishna, Guiguang Ding, Kurt Keutzer. CVPR 2017, 1529-1538, 2017. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), pp. Proceedings of the Third Conference on Medical Imaging with Deep Learning Held in Montreal, QC, Canada on 06-08 July 2020 Published as Volume 121 by the Proceedings of Machine Learning Research on 21 September 2020. , 1983 ). AAAI 2019. Conditional Generative Adversarial Network for Structured Domain Adaptation . sGAN, stacked generative adversarial network; cGAN, conditional generative adversarial network. Secondly, we introduce a feature-pyramid adversarial network to distill the high-level features and improve the … Asymmetric Tri-training for Unsupervised Domain … 5. Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, Shao-Ping Lu Pages 10869-10876 | PDF. The spirit behind is from generative adversarial learning [7], that trains two model-s, i.e., a generative model and a discriminative model, by pitting them against each other. The feature pyramid network was proposed as a top-down multi-scale feature extractor for extracting semantically rich features, which are used in object detectors, such as Faster R-CNN . 2019. In general, they help us achieve universality. DeceptionNet: Network-Driven Domain Randomization arXiv_CV arXiv_CV Adversarial GAN Pose_Estimation Optimization Classification Recognition 2019-04-04 Thu. Towards a Universal Appearance for Domain Generalization via Adversarial Learning 82: Runtong Zhang, Yuchen Wu and Keiji Yanai. Adversarial Pyramid Network for Video Domain Generalization We can note that the PSNR and SSIM have about 1.61 db, 6.7% improvement, respectively. In 33rd AAAI Conference on Artificial Intelligence ( AAAI ), 2019: 9030-9037. A Neural Network for Detailed Human Depth Estimation From a Single Image, Sicong Tang 14. (81%) Yudi Dong; Huaxia Wang; Yu-Dong Yao A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models. We present a method for training Domain-adversarial neural networks. Generating Low-Rank Textures via Generative Adversarial Network: S Zhao, J Li 2017 CyCADA: Cycle-Consistent Adversarial Domain Adaptation: J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko 2017 Malware Detection Using Deep Transferred Generative Adversarial Networks: JY Kim, SJ Bu, SB Cho 2017 Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation Yixin Zhang, Zilei Wang Pages 6877-6884 | PDF. 3490-3499 A Video Compression Framework Using an Overfitted Restoration Neural Network pp. A patient is scanned by a magnetic resonance imaging system to acquire magnetic resonance data. Kundu et al. Learning generative adversarial networks : next-generation deep learning simplified Ganguly, Kuntal. Domain adaptation bridges the reality gap by directly resolving differences between the domains . Research Papers Awesome - Most Cited Deep Learning Papers. The optic disc(OD) and the optic cup(OC) segmentation is an key step in fundus medical image analysis. Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data. Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta). GANs can synthesize images/videos from latent noise with a minimized adversarial cost function. They use a pre-trained Inception network to produce an embedding of the model samples, then compute the t-SNE on the Inception embeddings. 2014-06-10 | [Theory] Ian J. Goodfellow et al. In 2014, Ian Goodfellow et al. GANs are generative models: they create new data instances that resemble your training data. 593-597 Improving the affordability of robustness training for DNNs pp. Object detection typically assumes that training and test samples are drawn from an identical distribution, which, however, does not always hold in practice. a discriminator sub-network D 1 (x) that distinguishes whether an input image is HR or LR, and 1-D 1 (x) denotes the image x is LR; 4) a discriminator sub-network D 2 (x) that distinguishes whether an input image obeys the original (true) distribution P true (x), and 1-D 2 (x) denotes the image x is consistent with the P true (x). Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. Image created using gifify.Source: YouTube Welcome back to deep lear n ing to the last video where we discussed the different algorithms regarding generative adversarial networks. those defined in MPEG standards, as the low decoding complexity of vector quantization has become less relevant. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as … Y Chen, W Li, C Sakaridis, D Dai, L Van Gool However, the emergence of a large number of vehicles poses the critical but challenging problem of vehicle re-identification (reID). Pyramid module: The Gauss‐Laplacian pyramid is introduced into our network. Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification with K-Means Features Please enjoy it! Google Scholar; Kei Akuzawa, Yusuke Iwasawa, and Yutaka Matsuo. IEEE Transactions on Neural Networks and Learning Systems , 29(2), 259-272. Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data International Conference on Computer Vision (ICCV) , 2019 X Yue, Y Zhang, S Zhao, A Sangiovanni-Vincentelli, K Keutzer, and B Gong In the 3D vision domain, Hane et al. Sankaranarayanan et al. Computer vision is one of the hottest research fields in deep learning. arXiv preprint arXiv:1912.03716, 2019. Lai et al. Y Wang, M Long, J Wang, PS Yu. versarial learning for domain adaptation, which is to mod-el domain distribution via an adversarial objective with re-spect to a domain discriminator. There were also some questions about the t-SNE images. adversarial video compression guided by soft edge detection: 2055: ... cp-gan: context pyramid generative adversarial network for speech enhancement: 2498: ... domain adaptation for generalization of face presentation attack detection in mobile settings with minimal information: Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada. Domain Generalization via Entropy Regularization: Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao: link: 104: Robust Pre-Training by Adversarial Contrastive Learning: Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang: link: 105: DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation Chengyou Fang, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, Heng Huang. In: Proc. 6. The Laplacian Pyramid does perform a blur then sub-space reduction. The results are shown in Table 4. My description was off a bit. Cycle Self-Training for Domain Adaptation Hong Liu, Jianmin Wang, Mingsheng Long* Bi-tuning of Pre-trained Representations Jincheng Zhong, Ximei Wang, Zhi Kou, Jianmin Wang, Mingsheng Long* Adversarial Pyramid Network for Video Domain Generalization Zhiyu Yao, Yunbo Wang, Xingqiang Du, Mingsheng Long*, Jianmin Wang MViT is a multi-stage architecture. Image to Image Translation for Domain Adaptation . Group 1 2. Fortunately, unsupervised learning represented by clustering algorithm provides ideas for solving such problems [19, 20]. creating adversarial samples) is the best response to the other player, we propose a novel extension of a game-theoretic algorithm, namely fictitious play, to the domain of training robust classifiers. Towards a Universal Appearance for Domain Generalization via Adversarial Learning 82: Runtong Zhang, Yuchen Wu and Keiji Yanai. The results are shown in Table 4. Unseen Target Stance Detection with Adversarial Domain Generalization [#20153] Zhen Wang, Qiansheng Wang, Chengguo Lv, Xue Cao and Guohong Fu: Heilongjiang University, China; Institute of Artificial Intelligence, Soochow University, China: 8:00PM: Dynamic Global-Local Attention Network Based On Capsules for Text Classification [#20060] a generative adversarial network for medical image fusion: 1363: a generative self-ensemble approach to simulated+unsupervised learning: 2490: a lightweight network model for video frame interpolation using spatial pyramids: 2484: a model learning approach for low light image restoration: 2558 Unsupervised representation learning with deep convolutional generative adversarial networks 2. The kink in the function is the source of the non-linearity. The neural network was compared with the traditional bivariate interpolation method, and the results show that pix2pix is a valid alternative. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network • SRGAN, a generative adversarial network (GAN) for image superresolution (SR). The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. UU-Nets Connecting Discriminator and Generator for Image to Image Translation arXiv_CV arXiv_CV Adversarial GAN In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. domain adaptation algorithms that attempt to map repre-sentations between the two domains or learn to extract fea-tures that are domain–invariant. Employing a conditional generative adversarial network (c-GAN), known as pix2pix for T1-w brain images, we were able to reconstruct downsampled images till 10-fold. Photo-realistic single image super-resolution using a generative adversarial network. We used an ultrasound phantom for dosimetry verification. The spirit behind is from generative adversarial learning [7], that trains two model-s, i.e., a generative model and a discriminative model, by pitting them against each other. Towards a Universal Appearance for Domain Generalization via Adversarial Learning: 40: Liang Feng, Hiroaki Igarashi, Seiya Shibata, Yuki Kobayashi, Takashi Takenaka and Wei Zhang: Real-time Detection and Tracking using Hybrid DNNs and Space-aware Color Feature: from Algorithm to System: 47: Qier Meng, Shin'Ichi Satoh and Yohei Hashimoto 8. ... neural network 22. Adversarial Pyramid Network for Video Domain Generalization. TADA簡介 - Transferable Attention for Domain Adaptation 12 Dec; PADA簡介 - Partial Adversarial Domain Adaptation 10 Dec; GAN Dissection簡介 - Visualizing and Understanding Generative Adversarial Networks 04 Dec; M2Det簡介 - A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network 20 Nov Unfortunately, there is a technique called adversarial attack, which allows deceiving almost any neural network-based systems in some instances. Pre-trained and Shared Encoder in Cycle-Consistent Adversarial Networks to Improve Image Quality 145: Tomoyuki Shimizu, Jianfeng Xu and Kazuyuki Tasaka. September 2, 2020 (Wednesday) Time: 9:30am-10:30am Zoom Meeting ID: 973 7710 3589 Paper 1: Sean Kulinski Understanding Self-Training for Gradual Domain Adaptation These differences can be reduced by designing the target domain to generate network, training process through the domain discriminant and performing generator reconstruction between source domain and target domain. the feature distributions between the source domain and the target domain. [39] gives an overview of domain adaptation and transfer learning with a specific view on visual applications. 12272-12281 ESIR: End-To-End Scene Text Recognition via Iterative Image Rectification pp. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords and find related papers and patents.. International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world.

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