Generative Networks. James Hays Computer Vision
|
|
- Roy Watson
- 6 years ago
- Views:
Transcription
1 Generative Networks James Hays Computer Vision
2 Interesting Illusion: Ames Window
3 Recap Unsupervised Learning Style Transfer A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge. CVPR 2016.
4 Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei (Alyosha) Efros richzhang.github.io/colorization
5 Ansel Adams, Yosemite Valley Bridge
6 Ansel Adams, Yosemite Valley Bridge Our Result
7 Grayscale image: L channel Color information: ab channels L ab
8 Semantics? Higher-level Grayscale image: L channel abstraction? Concatenate (L,ab) L ab Free supervisory signal
9 Inherent Ambiguity Grayscale
10 Inherent Ambiguity Our Output Ground Truth
11 Better Loss Function Colors in ab space (continuous) Regression with L2 loss inadequate Use multinomial classification Class rebalancing to encourage learning of rare colors
12 Better Loss Function Colors in ab space (discrete) Regression with L2 loss inadequate Use multinomial classification Class rebalancing to encourage learning of rare colors
13
14 Better Loss Function Regression with L2 loss inadequate log 10 probability Histogram over ab space Use multinomial classification Class rebalancing to encourage learning of rare colors
15 Better Loss Function Regression with L2 loss inadequate log 10 probability Histogram over ab space Use multinomial classification Class rebalancing to encourage learning of rare colors
16 Classification Parametric L2 Regression Nonparametric Hertzmann et al. In SIGGRAPH, Welsh et al. In TOG, Irony et al. In Eurographics, Liu et al. In TOG, Chia et al. In ACM Gupta et al. In ACM, Hand-engineered Features Deep Networks Deshpande et al. Cheng et al. In ICCV Dahl. Jan Iizuka et al. In SIGGRAPH, Charpiat et al. In ECCV Larsson et al. In ECCV [Concurrent]
17 Network Architecture conv1 conv2 conv3 conv4 conv5 fc6 fc7 lightness 64 ab color L
18 Network Architecture lightness conv1 conv2 conv3 conv4 64 conv5 à trous [1]/dilated [2] conv6 à trous/dilated conv7 conv8 ab color L [1] Chen et al. In arxiv, [2] Yu and Koltun. In ICLR, 2016
19 Ground Input Truth L2 Regression Class w/ Rebalancing
20 Failure Cases
21 Biases
22 Evaluation Visual Quality Representation Learning Quantitative Qualitative Per-pixel accuracy Perceptual realism Semantic interpretability Low-level stimuli Legacy grayscale photos Task generalization ImageNet classification Task & dataset generalization PASCAL classification, detection, segmentation Hidden unit activations
23 Evaluation Visual Quality Representation Learning Quantitative Qualitative Per-pixel accuracy Perceptual realism Semantic interpretability Low-level stimuli Legacy grayscale photos Task generalization ImageNet classification Task & dataset generalization PASCAL classification, detection, segmentation Hidden unit activations
24 Evaluation Visual Quality Representation Learning Quantitative Qualitative Per-pixel accuracy Perceptual realism Semantic interpretability Low-level stimuli Legacy grayscale photos Task generalization ImageNet classification Task & dataset generalization PASCAL classification, detection, segmentation Hidden unit activations
25 Perceptual Realism / Amazon Mechanical Turk Test
26
27 clap if fake clap if fake
28 Fake, 0% fooled
29
30 clap if fake clap if fake
31 Fake, 55% fooled
32
33 clap if fake clap if fake
34 Fake, 58% fooled
35 from Reddit /u/sherysantucci
36 Recolorized by Reddit ColorizeBot
37 Photo taken by Reddit /u/timteroo, Mural from street artist Eduardo Kobra
38 Recolorized by Reddit ColorizeBot
39 Perceptual Realism Test 50% AMT Labeled Real [%] Ground Truth Random Larsson et al Ours (L2) Ours (class) Ours (full) images tested per algorithm %
40 Input Ground Truth Output
41 Does the method work on legacy black and white photos?
42 Thylacine, Dr. David Fleay, extinct in 1936.
43 Thylacine, Dr. David Fleay, extinct in 1936.
44 Amateur Family Photo, 1956.
45 Amateur Family Photo, 1956.
46 Henri Cartier-Bresson, Sunday on the Banks of the River Seine, 1938.
47 Henri Cartier-Bresson, Sunday on the Banks of the River Seine, 1938.
48 Dorothea Lange, Migrant Mother, 1936.
49 Dorothea Lange, Migrant Mother, 1936.
50 Additional Information Demo Reddit ColorizeBot Type colorizebot under any image post Code Website full paper, user examples, visualizations
51 For the full paper, additional examples and our model: richzhang.github.io/colorization
52 Small Sample of other Generative Networks of interest DCGAN. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala Generative Adversarial Text-to-Image Synthesis [PDF][Supplement][BibTex][Code] Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee. ICML Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
Unsupervised Deep Learning. James Hays slides from Carl Doersch and Richard Zhang
Unsupervised Deep Learning James Hays slides from Carl Doersch and Richard Zhang Recap from Previous Lecture We saw two strategies to get structured output while using deep learning With object detection,
More informationGenerative Adversarial Network
Generative Adversarial Network Many slides from NIPS 2014 Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio Generative adversarial
More informationDeep Learning for Visual Manipulation and Synthesis
Deep Learning for Visual Manipulation and Synthesis Jun-Yan Zhu 朱俊彦 UC Berkeley 2017/01/11 @ VALSE What is visual manipulation? Image Editing Program input photo User Input result Desired output: stay
More informationarxiv: v1 [cs.cv] 5 Jul 2017
AlignGAN: Learning to Align Cross- Images with Conditional Generative Adversarial Networks Xudong Mao Department of Computer Science City University of Hong Kong xudonmao@gmail.com Qing Li Department of
More informationColorful Image Colorization
Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros {rich.zhang,isola,efros}@eecs.berkeley.edu University of California, Berkeley Abstract. Given a grayscale photograph as input,
More informationLearning to Generate Images
Learning to Generate Images Jun-Yan Zhu Ph.D. at UC Berkeley Postdoc at MIT CSAIL Computer Vision before 2012 Cat Features Clustering Pooling Classification [LeCun et al, 1998], [Krizhevsky et al, 2012]
More informationarxiv: v2 [cs.cv] 13 Jun 2017
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN arxiv:1706.03319v2 [cs.cv] 13 Jun 2017 Lvmin Zhang, Yi Ji and Xin Lin School of Computer Science and Technology,
More informationarxiv: v1 [cs.cv] 28 Mar 2016
arxiv:1603.08511v1 [cs.cv] 28 Mar 2016 Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros {rich.zhang, isola, efros}@eecs.berkeley.edu University of California, Berkeley Abstract.
More informationWhat was Monet seeing while painting? Translating artworks to photo-realistic images M. Tomei, L. Baraldi, M. Cornia, R. Cucchiara
What was Monet seeing while painting? Translating artworks to photo-realistic images M. Tomei, L. Baraldi, M. Cornia, R. Cucchiara COMPUTER VISION IN THE ARTISTIC DOMAIN The effectiveness of Computer Vision
More informationGenerative Adversarial Text to Image Synthesis
Generative Adversarial Text to Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee Presented by: Jingyao Zhan Contents Introduction Related Work Method
More informationTowards Automatic Icon Design using Machine Learning
Moses Soh (msoh) moses.soh@gmail.com Abstract We propose a deep learning approach for automatic colorization of icons. The system maps grayscale outlines to fully colored and stylized icons using a Convolutional
More informationarxiv: v2 [cs.cv] 23 Dec 2017
TextureGAN: Controlling Deep Image Synthesis with Texture Patches Wenqi Xian 1 Patsorn Sangkloy 1 Varun Agrawal 1 Amit Raj 1 Jingwan Lu 2 Chen Fang 2 Fisher Yu 3 James Hays 1 1 Georgia Institute of Technology
More informationarxiv: v2 [cs.cv] 1 Dec 2017
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation Chao Wang Haiyong Zheng Zhibin Yu Ziqiang Zheng Zhaorui Gu Bing Zheng Ocean University of China Qingdao,
More informationProgress on Generative Adversarial Networks
Progress on Generative Adversarial Networks Wangmeng Zuo Vision Perception and Cognition Centre Harbin Institute of Technology Content Image generation: problem formulation Three issues about GAN Discriminate
More informationDeep Fakes using Generative Adversarial Networks (GAN)
Deep Fakes using Generative Adversarial Networks (GAN) Tianxiang Shen UCSD La Jolla, USA tis038@eng.ucsd.edu Ruixian Liu UCSD La Jolla, USA rul188@eng.ucsd.edu Ju Bai UCSD La Jolla, USA jub010@eng.ucsd.edu
More informationarxiv: v1 [cs.cv] 7 Feb 2018
Unsupervised Typography Transfer Hanfei Sun Carnegie Mellon University hanfeis@andrew.cmu.edu Yiming Luo Carnegie Mellon University yimingl1@andrew.cmu.edu Ziang Lu Carnegie Mellon University ziangl@andrew.cmu.edu
More informationTextureGAN: Controlling Deep Image Synthesis with Texture Patches
TextureGAN: Controlling Deep Image Synthesis with Texture Patches Wenqi Xian 1 Patsorn Sangkloy 1 Varun Agrawal 1 Amit Raj 1 Jingwan Lu 2 Chen Fang 2 Fisher Yu 3 James Hays 1,4 1 Georgia Institute of Technology
More informationToward Multimodal Image-to-Image Translation
Toward Multimodal Image-to-Image Translation Jun-Yan Zhu UC Berkeley Richard Zhang UC Berkeley Deepak Pathak UC Berkeley Trevor Darrell UC Berkeley Alexei A. Efros UC Berkeley Oliver Wang Adobe Research
More informationEncoder-Decoder Networks for Semantic Segmentation. Sachin Mehta
Encoder-Decoder Networks for Semantic Segmentation Sachin Mehta Outline > Overview of Semantic Segmentation > Encoder-Decoder Networks > Results What is Semantic Segmentation? Input: RGB Image Output:
More informationDOMAIN-ADAPTIVE GENERATIVE ADVERSARIAL NETWORKS FOR SKETCH-TO-PHOTO INVERSION
DOMAIN-ADAPTIVE GENERATIVE ADVERSARIAL NETWORKS FOR SKETCH-TO-PHOTO INVERSION Yen-Cheng Liu 1, Wei-Chen Chiu 2, Sheng-De Wang 1, and Yu-Chiang Frank Wang 1 1 Graduate Institute of Electrical Engineering,
More informationSYNTHESIS OF IMAGES BY TWO-STAGE GENERATIVE ADVERSARIAL NETWORKS. Qiang Huang, Philip J.B. Jackson, Mark D. Plumbley, Wenwu Wang
SYNTHESIS OF IMAGES BY TWO-STAGE GENERATIVE ADVERSARIAL NETWORKS Qiang Huang, Philip J.B. Jackson, Mark D. Plumbley, Wenwu Wang Centre for Vision, Speech and Signal Processing University of Surrey, Guildford,
More informationDeep Manga Colorization with Color Style Extraction by Conditional Adversarially Learned Inference
Information Engineering Express International Institute of Applied Informatics 2017, Vol.3, No.4, P.55-66 Deep Manga Colorization with Color Style Extraction by Conditional Adversarially Learned Inference
More informationDOMAIN-ADAPTIVE GENERATIVE ADVERSARIAL NETWORKS FOR SKETCH-TO-PHOTO INVERSION
2017 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 25 28, 2017, TOKYO, JAPAN DOMAIN-ADAPTIVE GENERATIVE ADVERSARIAL NETWORKS FOR SKETCH-TO-PHOTO INVERSION Yen-Cheng Liu 1,
More informationA Neural Algorithm of Artistic Style. Leon A. Gatys, Alexander S. Ecker, Mattthias Bethge Presented by Weidi Xie (1st Oct 2015 )
A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Mattthias Bethge Presented by Weidi Xie (1st Oct 2015 ) What does the paper do? 2 Create artistic images of high perceptual quality.
More informationHuman Pose Estimation with Deep Learning. Wei Yang
Human Pose Estimation with Deep Learning Wei Yang Applications Understand Activities Family Robots American Heist (2014) - The Bank Robbery Scene 2 What do we need to know to recognize a crime scene? 3
More informationImage-to-Image Translation with Conditional Adversarial Networks
Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros Berkeley AI Research (BAIR) Laboratory, UC Berkeley Labels to Street Scene Labels
More informationEnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Supplementary
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Supplementary Mehdi S. M. Sajjadi Bernhard Schölkopf Michael Hirsch Max Planck Institute for Intelligent Systems Spemanstr.
More informationarxiv: v1 [cs.cv] 17 Nov 2016
Inverting The Generator Of A Generative Adversarial Network arxiv:1611.05644v1 [cs.cv] 17 Nov 2016 Antonia Creswell BICV Group Bioengineering Imperial College London ac2211@ic.ac.uk Abstract Anil Anthony
More informationarxiv: v1 [cs.cv] 8 May 2018
arxiv:1805.03189v1 [cs.cv] 8 May 2018 Learning image-to-image translation using paired and unpaired training samples Soumya Tripathy 1, Juho Kannala 2, and Esa Rahtu 1 1 Tampere University of Technology
More informationarxiv: v1 [cs.cv] 3 Apr 2019
Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training Adam Nyberg 1[0000 0001 8764 8499], Abdelrahman Eldesokey 1[0000 0003 3292 7153], David Bergström 2[0000 0003 2414 4482], and David
More informationarxiv: v2 [cs.cv] 2 Dec 2017
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks Wei Xiong, Wenhan Luo, Lin Ma, Wei Liu, and Jiebo Luo Department of Computer Science, University of Rochester,
More informationColorNN Book: A Recurrent-Inspired Deep Learning Approach to Consistent Video Colorization
ColorNN Book: A Recurrent-Inspired Deep Learning Approach to Consistent Video Colorization Divyahans Gupta Stanford University Stanford, California 94305 dgupta2@stanford.edu Sanjay Kannan Stanford University
More informationGENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY
GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY Denis Volkhonskiy 2,3, Boris Borisenko 3 and Evgeny Burnaev 1,2,3 1 Skolkovo Institute of Science and Technology 2 The Institute for Information
More informationarxiv: v1 [eess.sp] 23 Oct 2018
Reproducing AmbientGAN: Generative models from lossy measurements arxiv:1810.10108v1 [eess.sp] 23 Oct 2018 Mehdi Ahmadi Polytechnique Montreal mehdi.ahmadi@polymtl.ca Mostafa Abdelnaim University de Montreal
More informationarxiv: v3 [cs.cv] 30 Mar 2018
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks Wei Xiong Wenhan Luo Lin Ma Wei Liu Jiebo Luo Tencent AI Lab University of Rochester {wxiong5,jluo}@cs.rochester.edu
More informationGENERATIVE ADVERSARIAL NETWORK-BASED VIR-
GENERATIVE ADVERSARIAL NETWORK-BASED VIR- TUAL TRY-ON WITH CLOTHING REGION Shizuma Kubo, Yusuke Iwasawa, and Yutaka Matsuo The University of Tokyo Bunkyo-ku, Japan {kubo, iwasawa, matsuo}@weblab.t.u-tokyo.ac.jp
More informationWord-Conditioned Image Style Transfer. Yu Sugiyama and Keiji Yanai The University of Electro-Communications, Tokyo
Word-Conditioned Image Style Transfer Yu Sugiyama and Keiji Yanai The University of Electro-Communications, Tokyo 1 Introduction Neural Style Transfer, Image style transfer 2018/11/27 UEC Yanai Lab. Tokyo.
More informationarxiv: v2 [cs.cv] 24 Jul 2017
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network Vedran Vukotić 1,2,3, Silvia-Laura Pintea 2, Christian Raymond 1,3, Guillaume Gravier 1,4, and
More informationarxiv: v1 [cs.cv] 22 Feb 2017
Synthesising Dynamic Textures using Convolutional Neural Networks arxiv:1702.07006v1 [cs.cv] 22 Feb 2017 Christina M. Funke, 1, 2, 3, Leon A. Gatys, 1, 2, 4, Alexander S. Ecker 1, 2, 5 1, 2, 3, 6 and Matthias
More informationA GAN framework for Instance Segmentation using the Mutex Watershed Algorithm
A GAN framework for Instance Segmentation using the Mutex Watershed Algorithm Mandikal Vikram National Institute of Technology Karnataka, India 15it217.vikram@nitk.edu.in Steffen Wolf HCI/IWR, University
More informationStarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation Yunjey Choi 1,2 Minje Choi 1,2 Munyoung Kim 2,3 Jung-Woo Ha 2 Sunghun Kim 2,4 Jaegul Choo 1,2 1 Korea University
More informationarxiv: v1 [cs.cv] 28 Jul 2017
Photographic Image Synthesis with Cascaded Refinement Networks Qifeng Chen Vladlen Koltun arxiv:1707.09405v1 [cs.cv] 28 Jul 2017 (a) Input semantic layouts (b) Synthesized images Figure 1. Given a pixelwise
More informationarxiv: v1 [cs.cv] 6 Sep 2018
arxiv:1809.01890v1 [cs.cv] 6 Sep 2018 Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial Networks Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto
More informationarxiv: v2 [cs.cv] 21 Jul 2018
Deep Exemplar-based Colorization Mingming He 1, Dongdong Chen 2, Jing Liao 3, Pedro V. Sander 1, and Lu Yuan 3 1 Hong Kong UST, 2 University of Science and Technology of China, 3 Microsoft Research arxiv:1807.06587v2
More informationConvolutional Neural Networks + Neural Style Transfer. Justin Johnson 2/1/2017
Convolutional Neural Networks + Neural Style Transfer Justin Johnson 2/1/2017 Outline Convolutional Neural Networks Convolution Pooling Feature Visualization Neural Style Transfer Feature Inversion Texture
More informationarxiv: v1 [cs.mm] 16 Mar 2017
Steganographic Generative Adversarial Networks arxiv:1703.05502v1 [cs.mm] 16 Mar 2017 Denis Volkhonskiy 1,2,3, Ivan Nazarov 1,2, Boris Borisenko 3 and Evgeny Burnaev 1,2,3 1 Skolkovo Institute of Science
More informationAdaptive Appearance Rendering
ZHAI ET AL.: ADAPTIVE APPEARANCE RENDERING 1 Adaptive Appearance Rendering Mengyao Zhai mzhai@sfu.ca Ruizhi Deng ruizhid@sfu.ca Jiacheng Chen jca348@sfu.ca Lei Chen chenleic@sfu.ca Zhiwei Deng zhiweid@sfu.ca
More informationA Neural Algorithm of Artistic Style. Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
A Neural Algorithm of Artistic Style Leon A. Gatys, Alexander S. Ecker, Matthias Bethge Presented by Shishir Mathur (1 Sept 2016) What is this paper This is the research paper behind Prisma It creates
More informationGenerative Models II. Phillip Isola, MIT, OpenAI DLSS 7/27/18
Generative Models II Phillip Isola, MIT, OpenAI DLSS 7/27/18 What s a generative model? For this talk: models that output high-dimensional data (Or, anything involving a GAN, VAE, PixelCNN, etc) Useful
More informationUnsupervised Cross-Domain Deep Image Generation
Unsupervised Cross-Domain Deep Image Generation Yaniv Taigman, Adam Polyak, Lior Wolf Facebook AI Research (FAIR) Tel Aviv Supervised Learning; {Xi, yi} àf Face Recognition (DeepFace / FAIR) Kaiming et
More informationarxiv: v2 [cs.cv] 25 Jul 2018
Unpaired Photo-to-Caricature Translation on Faces in the Wild Ziqiang Zheng a, Chao Wang a, Zhibin Yu a, Nan Wang a, Haiyong Zheng a,, Bing Zheng a arxiv:1711.10735v2 [cs.cv] 25 Jul 2018 a No. 238 Songling
More informationarxiv: v1 [cs.cv] 25 Aug 2018
Painting Outside the Box: Image Outpainting with GANs Mark Sabini and Gili Rusak Stanford University {msabini, gilir}@cs.stanford.edu arxiv:1808.08483v1 [cs.cv] 25 Aug 2018 Abstract The challenging task
More informationLecture 7: Semantic Segmentation
Semantic Segmentation CSED703R: Deep Learning for Visual Recognition (207F) Segmenting images based on its semantic notion Lecture 7: Semantic Segmentation Bohyung Han Computer Vision Lab. bhhanpostech.ac.kr
More informationProgressive Generative Hashing for Image Retrieval
Progressive Generative Hashing for Image Retrieval Yuqing Ma, Yue He, Fan Ding, Sheng Hu, Jun Li, Xianglong Liu 2018.7.16 01 BACKGROUND the NNS problem in big data 02 RELATED WORK Generative adversarial
More informationVisual Recommender System with Adversarial Generator-Encoder Networks
Visual Recommender System with Adversarial Generator-Encoder Networks Bowen Yao Stanford University 450 Serra Mall, Stanford, CA 94305 boweny@stanford.edu Yilin Chen Stanford University 450 Serra Mall
More informationHigh-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang1 Ming-Yu Liu1 Jun-Yan Zhu2 Andrew Tao1 Jan Kautz1 1 2 NVIDIA Corporation UC Berkeley Bryan Catanzaro1 Cascaded
More informationarxiv: v1 [cs.cv] 20 Mar 2017
I2T2I: LEARNING TEXT TO IMAGE SYNTHESIS WITH TEXTUAL DATA AUGMENTATION Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo arxiv:1703.06676v1 [cs.cv] 20 Mar 2017 Data Science Institute, Imperial College
More informationRecovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform. Xintao Wang Ke Yu Chao Dong Chen Change Loy
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform Xintao Wang Ke Yu Chao Dong Chen Change Loy Problem enlarge 4 times Low-resolution image High-resolution image Previous
More informationColorization Using ConvNet and GAN
Colorization Using ConvNet and GAN Qiwen Fu qiwenfu@stanford.edu Stanford Univeristy Wei-Ting Hsu hsuwt@stanford.edu Stanford University Mu-Heng Yang mhyang@stanford.edu Stanford University Abstract Colorization
More informationLearning Representations for Automatic Colorization
Learning Representations for Automatic Colorization Gustav Larsson 1, Michael Maire 2, and Gregory Shakhnarovich 2 1 University of Chicago 2 Toyota Technological Institute at Chicago larsson@cs.uchicago.edu,
More informationarxiv: v1 [cs.cv] 1 Nov 2018
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning Wenbin Li, Wei Xiong, Haofu Liao, Jing Huo, Yang Gao, Jiebo Luo State Key Laboratory for Novel Software Technology, Nanjing University,
More informationarxiv: v1 [cs.cv] 30 Nov 2017
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs arxiv:1711.11585v1 [cs.cv] 30 Nov 017 Ting-Chun Wang1 Ming-Yu Liu1 Jun-Yan Zhu Andrew Tao1 Jan Kautz1 1 NVIDIA Corporation
More informationTwo Routes for Image to Image Translation: Rule based vs. Learning based. Minglun Gong, Memorial Univ. Collaboration with Mr.
Two Routes for Image to Image Translation: Rule based vs. Learning based Minglun Gong, Memorial Univ. Collaboration with Mr. Zili Yi Introduction A brief history of image processing Image to Image translation
More informationarxiv: v1 [cs.cv] 11 Oct 2018
SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning Xiaoming Yu, Xing Cai, Zhenqiang Ying, Thomas Li, and Ge Li arxiv:1810.04991v1 [cs.cv]
More informationInfrared Image Colorization based on a Triplet DCGAN Architecture
Infrared Image Colorization based on a Triplet DCGAN Architecture Patricia L. Suárez plsuarez@espol.edu.ec Angel D. Sappa,2 sappa@ieee.org Boris X. Vintimilla boris.vintimilla@espol.edu.ec Escuela Superior
More informationarxiv: v1 [stat.ml] 14 Sep 2017
The Conditional Analogy GAN: Swapping Fashion Articles on People Images Nikolay Jetchev Zalando Research nikolay.jetchev@zalando.de Urs Bergmann Zalando Research urs.bergmann@zalando.de arxiv:1709.04695v1
More informationREGION AVERAGE POOLING FOR CONTEXT-AWARE OBJECT DETECTION
REGION AVERAGE POOLING FOR CONTEXT-AWARE OBJECT DETECTION Kingsley Kuan 1, Gaurav Manek 1, Jie Lin 1, Yuan Fang 1, Vijay Chandrasekhar 1,2 Institute for Infocomm Research, A*STAR, Singapore 1 Nanyang Technological
More informationGenerative Semantic Manipulation with Contrasting GAN
Generative Semantic Manipulation with Contrasting GAN Xiaodan Liang, Hao Zhang, Eric P. Xing Carnegie Mellon University and Petuum Inc. {xiaodan1, hao, epxing}@cs.cmu.edu arxiv:1708.00315v1 [cs.cv] 1 Aug
More informationExploring Style Transfer: Extensions to Neural Style Transfer
Exploring Style Transfer: Extensions to Neural Style Transfer Noah Makow Stanford University nmakow@stanford.edu Pablo Hernandez Stanford University pabloh2@stanford.edu Abstract Recent work by Gatys et
More informationarxiv: v3 [cs.cv] 24 Nov 2017
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu Taesung Park Phillip Isola Alexei A. Efros Berkeley AI Research (BAIR) laboratory, UC Berkeley arxiv:1703.10593v3
More informationIDENTIFYING ANALOGIES ACROSS DOMAINS
IDENTIFYING ANALOGIES ACROSS DOMAINS Yedid Hoshen 1 and Lior Wolf 1,2 1 Facebook AI Research 2 Tel Aviv University ABSTRACT Identifying analogies across domains without supervision is an important task
More informationFast Patch-based Style Transfer of Arbitrary Style
Fast Patch-based Style Transfer of Arbitrary Style Tian Qi Chen Department of Computer Science University of British Columbia tqichen@cs.ubc.ca Mark Schmidt Department of Computer Science University of
More informationAdversarial Scene Editing: Automatic Object Removal from Weak Supervision
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision Rakshith Shetty 1 Mario Fritz 2 Bernt Schiele 1 1 Max Planck Institute for Informatics, Saarland Informatics Campus 2 CISPA Helmholtz
More informationarxiv: v2 [cs.cv] 20 Aug 2018
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang 1 Ming-Yu Liu 1 Jun-Yan Zhu 2 Andrew Tao 1 Jan Kautz 1 Bryan Catanzaro 1 1 NVIDIA Corporation 2 UC Berkeley
More informationDirect Multi-Scale Dual-Stream Network for Pedestrian Detection Sang-Il Jung and Ki-Sang Hong Image Information Processing Lab.
[ICIP 2017] Direct Multi-Scale Dual-Stream Network for Pedestrian Detection Sang-Il Jung and Ki-Sang Hong Image Information Processing Lab., POSTECH Pedestrian Detection Goal To draw bounding boxes that
More informationarxiv: v1 [cs.cv] 1 Nov 2018
Examining Performance of Sketch-to-Image Translation Models with Multiclass Automatically Generated Paired Training Data Dichao Hu College of Computing, Georgia Institute of Technology, 801 Atlantic Dr
More informationUnsupervised Person Image Synthesis in Arbitrary Poses
Unsupervised Person Image Synthesis in Arbitrary Poses Albert Pumarola Antonio Agudo Alberto Sanfeliu Francesc Moreno-Noguer Institut de Robòtica i Informàtica Industrial (CSIC-UPC) 08028, Barcelona, Spain
More informationAutomatic Colorization with Improved Spatial Coherence and Boundary Localization
Zhang W, Fang CW, Li GB. Automatic colorization with improved spatial coherence and boundary localization. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 32(3): 494 506 May 2017. DOI 10.1007/s11390-017-1739-6
More informationarxiv: v2 [cs.lg] 7 Jun 2017
Pixel Deconvolutional Networks Hongyang Gao Washington State University Pullman, WA 99164 hongyang.gao@wsu.edu Hao Yuan Washington State University Pullman, WA 99164 hao.yuan@wsu.edu arxiv:1705.06820v2
More informationAdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation Introduction Supplementary material In the supplementary material, we present additional qualitative results of the proposed AdaDepth
More informationarxiv: v1 [cs.cv] 29 Nov 2017
Photo-to-Caricature Translation on Faces in the Wild Ziqiang Zheng Haiyong Zheng Zhibin Yu Zhaorui Gu Bing Zheng Ocean University of China arxiv:1711.10735v1 [cs.cv] 29 Nov 2017 Abstract Recently, image-to-image
More informationarxiv: v3 [cs.cv] 13 Aug 2017
arxiv:1603.06668v3 [cs.cv] 13 Aug 2017 Learning Representations for Automatic Colorization Gustav Larsson 1, Michael Maire 2, and Gregory Shakhnarovich 2 1 University of Chicago 2 Toyota Technological
More informationarxiv: v1 [cs.cv] 26 Jul 2016
Semantic Image Inpainting with Perceptual and Contextual Losses arxiv:1607.07539v1 [cs.cv] 26 Jul 2016 Raymond Yeh Chen Chen Teck Yian Lim, Mark Hasegawa-Johnson Minh N. Do Dept. of Electrical and Computer
More informationarxiv: v1 [cs.cv] 14 Jun 2018
Single Image Separation with Perceptual Losses Xuaner Zhang UC Berkeley Ren Ng UC Berkeley Qifeng Chen Intel Labs arxiv:1806.05376v1 [cs.cv] 14 Jun 2018 Abstract We present an approach to separating reflection
More information3D Object Recognition and Scene Understanding from RGB-D Videos. Yu Xiang Postdoctoral Researcher University of Washington
3D Object Recognition and Scene Understanding from RGB-D Videos Yu Xiang Postdoctoral Researcher University of Washington 1 2 Act in the 3D World Sensing & Understanding Acting Intelligent System 3D World
More informationLearning from 3D Data
Learning from 3D Data Thomas Funkhouser Princeton University* * On sabbatical at Stanford and Google Disclaimer: I am talking about the work of these people Shuran Song Andy Zeng Fisher Yu Yinda Zhang
More informationSSD: Single Shot MultiBox Detector. Author: Wei Liu et al. Presenter: Siyu Jiang
SSD: Single Shot MultiBox Detector Author: Wei Liu et al. Presenter: Siyu Jiang Outline 1. Motivations 2. Contributions 3. Methodology 4. Experiments 5. Conclusions 6. Extensions Motivation Motivation
More informationarxiv: v1 [cs.cv] 8 Jan 2019
GILT: Generating Images from Long Text Ori Bar El, Ori Licht, Netanel Yosephian Tel-Aviv University {oribarel, oril, yosephian}@mail.tau.ac.il arxiv:1901.02404v1 [cs.cv] 8 Jan 2019 Abstract Creating an
More informationMulti-Task Self-Supervised Visual Learning
Multi-Task Self-Supervised Visual Learning Sikai Zhong March 4, 2018 COMPUTER SCIENCE Table of contents 1. Introduction 2. Self-supervised Tasks 3. Architectures 4. Experiments 1 Introduction Self-supervised
More informationSynscapes A photorealistic syntehtic dataset for street scene parsing Jonas Unger Department of Science and Technology Linköpings Universitet.
Synscapes A photorealistic syntehtic dataset for street scene parsing Jonas Unger Department of Science and Technology Linköpings Universitet 7D Labs VINNOVA https://7dlabs.com Photo-realistic image synthesis
More informationUnpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu Taesung Park Phillip Isola Alexei A. Efros Berkeley AI Research (BAIR) laboratory, UC Berkeley Monet Photos Zebras
More informationDecoder Network over Lightweight Reconstructed Feature for Fast Semantic Style Transfer
Decoder Network over Lightweight Reconstructed Feature for Fast Semantic Style Transfer Ming Lu 1, Hao Zhao 1, Anbang Yao 2, Feng Xu 3, Yurong Chen 2, and Li Zhang 1 1 Department of Electronic Engineering,
More informationarxiv: v2 [cs.cv] 23 Sep 2018
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks Xueqing Deng University of California, Merced
More informationUnpaired Multi-Domain Image Generation via Regularized Conditional GANs
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs Xudong Mao and Qing Li Department of Computer Science, City University of Hong Kong xudong.xdmao@gmail.com, itqli@cityu.edu.hk information
More informationGANosaic: Mosaic Creation with Generative Texture Manifolds
GANosaic: Mosaic Creation with Generative Texture Manifolds Nikolay Jetchev nikolay.jetchev@zalando.de Zalando Research Urs Bergmann urs.bergmann@zalando.de Zalando Research Abstract Calvin Seward calvin.seward@zalando.de
More informationIMPROVING DOCUMENT BINARIZATION VIA ADVERSARIAL NOISE-TEXTURE AUGMENTATION
IMPROVING DOCUMENT BINARIZATION VIA ADVERSARIAL NOISE-TEXTURE AUGMENTATION Ankan Kumar Bhunia 1, Ayan Kumar Bhunia 2, Aneeshan Sain 3, Partha Pratim Roy 4 1 Jadavpur University, India 2 Nanyang Technological
More informationGenerative Semantic Manipulation with Mask-Contrasting GAN
Generative Semantic Manipulation with Mask-Contrasting GAN Xiaodan Liang 1, Hao Zhang 1, Liang Lin 2, and Eric Xing 1 1 Carnegie Mellon University, {xiaodan1, hao, epxing}@cs.cmu.edu 2 Sun Yat-sen University,
More informationUnpaired Multi-Domain Image Generation via Regularized Conditional GANs
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs Xudong Mao and Qing Li Department of Computer Science, City University of Hong Kong xudong.xdmao@gmail.com, itqli@cityu.edu.hk Abstract
More informationSeparating Style and Content for Generalized Style Transfer
Separating Style and Content for Generalized Style Transfer Yexun Zhang Shanghai Jiao Tong University zhyxun@sjtu.edu.cn Ya Zhang Shanghai Jiao Tong University ya zhang@sjtu.edu.cn Wenbin Cai Microsoft
More informationarxiv: v1 [cs.cv] 19 Oct 2017
Be Your Own Prada: Fashion Synthesis with Structural Coherence Shizhan Zhu 1 Sanja Fidler 2,3 Raquel Urtasun 2,3,4 Dahua Lin 1 Chen Change Loy 1 1 Department of Information Engineering, The Chinese University
More information