Sky is Not the Limit: Semantic-Aware Sky Replacement
|
|
- Cornelius Porter
- 6 years ago
- Views:
Transcription
1 Sky is Not the Limit: Semantic-Aware Sky Replacement ACM Transactions on Graphics (SIGGRAPH), 2016 Yi-Hsuan Tsai UC Merced Xiaohui Shen Adobe Research Zhe Lin Adobe Research Kalyan Sunkavalli Adobe Research Ming-Hsuan Yang UC Merced 1
2 Input image 2
3 Input image 3
4 Input image Our result Automatic 4
5 Input image Diverse & Realistic! Automatic 5
6 6
7 7
8 8
9 9
10 Overview Input image Sky Segmentation Sky Search Sky Replacement Output images 10
11 Previous work: sky segmentation Sky/non-sky classifier [Tao et al. SIGGRAPH 09] Scene parsing [Long et al. CVPR 15] Input image [Long et al. CVPR 15] 11
12 Previous work: sky image search GIST [Hays and Efros SIGGRAPH 07, Liu et al. CGF 14] Limitation: global scene layout Input image Reference image 1 Reference image 2 12
13 Previous work: appearance transfer Global transfer [Reinhard et al. 2001, Tao et al. SIGGRAPH 09] Local transfer [Wu et al. CGF 13, Laffont et al. SIGGRAPH 14] Input image Input image with replaced sky 13
14 Previous work: appearance transfer Global transfer [Reinhard et al. 2001, Tao et al. SIGGRAPH 09] Local transfer [Wu et al. CGF 13, Laffont et al. SIGGRAPH 14] Input image Global transfer 14
15 Key idea: semantic guidance Input image Sky Segmentation Sky Search Sky Replacement Semantic Scene Parsing 15
16 Semantic Scene Parsing [Long et al. CVPR 15] Pixel-wise segmentation Semantic response Scene Parsing Sky Tree Building Fg Road Semantic Response... Sky Building Road 16
17 Input image Sky Segmentation Sky Search Sky Replacement Semantic Scene Parsing 17
18 Sky Segmentation Input image Scene Parsing Online Refinement Online model for sky Color, texture, semantics Better than dense CRF Alpha matte 18
19 Sky Segmentation Results 19
20 Input image Results from scene parsing Our refined results 20
21 21
22 22
23 Comparison to DeepLab [Chen et al. 2015]
24 Input image Sky Segmentation Sky Search Sky Replacement Semantic Scene Parsing 28
25 Sky Image Search Sky Image Database (415 Images) Input image 29
26 Sky Image Search Sky Image Database (415 Images) Input image Sky Search Semantic layout descriptor Account for local content 30
27 Semantic Layout Descriptor Input image Sky Building Road... Semantic responses Pixel-wise responses Normalize: from 0 to 1 31
28 Semantic Layout Descriptor Input image Sky Building Road... Average pooling Global info Local content Descriptor... 32
29 Input image Sky Segmentation Sky Search Sky Replacement Semantic Scene Parsing 33
30 Sky Replacement Input image Outputs Sky Alignment Semantic-aware Transfer Reference images Semantic-aware Transfer Adjust foreground appearance Account for semantic regions 34
31 Semantic-aware Transfer Propose a soft mapping method Weighted transfer for category n on pixel x Sky Building Road Pixel x T(x) = 0.45*T sky (x) *T bld (x) *T road (x) 35
32 Semantic-aware Transfer Transfer Function T n for category n Transfer luminance and color Not all the semantic regions are matched! T 1 (x) T 2 (x)? Color Matched Use chrominance Non-matched Use color temperature 36
33 Semantic-aware Transfer Input image Scene parsing Our result 37
34 Sky Replacement Results 38
35 39
36 Input image 40
37 41
38 42
39 43
40 44
41 Sky Replacement with User Preference 45
42 Input image 46
43 Input image 47
44 Input image Preferred sky 48
45 Performance Evaluation 49
46 Comparisons of different transfer methods Input image Reference [Tao, et al. 2009] Ours (w/o semantics) Our method 50
47 Comparisons of different transfer methods Input image Reference [Tao, et al. 2009] Ours (w/o semantics) Our method 51
48 More realistic 52
49 Comparisons of different search methods Random selection Input image Our method 53
50 Comparisons of different search methods Random selection Input image Our method 54
51 Comparisons of different search methods GIST based method Input image Our method 55
52 More realistic and interesting 56
53 Limitation Input image Output image 57
54 Conclusions Project website Automatic sky replacement Realistic results Semantics helps a lot Sky segmentation Sky image search Appearance transfer 58
Sky is Not the Limit: Semantic-Aware Sky Replacement
Sky is Not the Limit: Semantic-Aware Sky Replacement Yi-Hsuan Tsai 1 Xiaohui Shen 2 Zhe Lin 2 Kalyan Sunkavalli 2 Ming-Hsuan Yang 1 1 University of California, Merced 2 Adobe Research Analysis of sky segmentation
More informationDeep Image Harmonization
Deep Image Harmonization Yi-Hsuan Tsai 1 Xiaohui Shen 2 Zhe Lin 2 Kalyan Sunkavalli 2 Xin Lu 2 Ming-Hsuan Yang 1 1 University of California, Merced 2 Adobe Research 1 {ytsai2, mhyang}@ucmerced.edu 2 {xshen,
More informationScene Parsing with Global Context Embedding
Scene Parsing with Global Context Embedding Wei-Chih Hung 1, Yi-Hsuan Tsai 1,2, Xiaohui Shen 3, Zhe Lin 3, Kalyan Sunkavalli 3, Xin Lu 3, Ming-Hsuan Yang 1 1 University of California, Merced 2 NEC Laboratories
More informationSingle Image Super-resolution. Slides from Libin Geoffrey Sun and James Hays
Single Image Super-resolution Slides from Libin Geoffrey Sun and James Hays Cs129 Computational Photography James Hays, Brown, fall 2012 Types of Super-resolution Multi-image (sub-pixel registration) Single-image
More informationSwitchable Temporal Propagation Network
Switchable Temporal Propagation Network Sifei Liu 1, Guangyu Zhong 1,3, Shalini De Mello 1, Jinwei Gu 1 Varun Jampani 1, Ming-Hsuan Yang 2, Jan Kautz 1 1 NVIDIA, 2 UC Merced, 3 Dalian University of Technology
More informationSkyFinder: Attribute-based Sky Image Search
SkyFinder: Attribute-based Sky Image Search SIGGRAPH 2009 Litian Tao, Lu Yuan, Jian Sun Kim, Wook 2016. 1. 12 Abstract Interactive search system of over a half million sky images Automatically extracted
More informationCan Similar Scenes help Surface Layout Estimation?
Can Similar Scenes help Surface Layout Estimation? Santosh K. Divvala, Alexei A. Efros, Martial Hebert Robotics Institute, Carnegie Mellon University. {santosh,efros,hebert}@cs.cmu.edu Abstract We describe
More informationCascade Region Regression for Robust Object Detection
Large Scale Visual Recognition Challenge 2015 (ILSVRC2015) Cascade Region Regression for Robust Object Detection Jiankang Deng, Shaoli Huang, Jing Yang, Hui Shuai, Zhengbo Yu, Zongguang Lu, Qiang Ma, Yali
More informationPredicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture David Eigen, Rob Fergus
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture David Eigen, Rob Fergus Presented by: Rex Ying and Charles Qi Input: A Single RGB Image Estimate
More informationSegmentation. Bottom up Segmentation Semantic Segmentation
Segmentation Bottom up Segmentation Semantic Segmentation Semantic Labeling of Street Scenes Ground Truth Labels 11 classes, almost all occur simultaneously, large changes in viewpoint, scale sky, road,
More informationDeep learning for dense per-pixel prediction. Chunhua Shen The University of Adelaide, Australia
Deep learning for dense per-pixel prediction Chunhua Shen The University of Adelaide, Australia Image understanding Classification error Convolution Neural Networks 0.3 0.2 0.1 Image Classification [Krizhevsky
More informationWeb-Scale Image Search and Their Applications
Web-Scale Image Search and Their Applications Sung-Eui Yoon KAIST http://sglab.kaist.ac.kr Project Guidelines: Project Topics Any topics related to the course theme are okay You can find topics by browsing
More informationLearning Deep Structured Models for Semantic Segmentation. Guosheng Lin
Learning Deep Structured Models for Semantic Segmentation Guosheng Lin Semantic Segmentation Outline Exploring Context with Deep Structured Models Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel;
More informationAutomatic Generation of An Infinite Panorama
Automatic Generation of An Infinite Panorama Lisa H. Chan Alexei A. Efros Carnegie Mellon University Original Image Scene Matches Output Image Figure 1: Given an input image, scene matching from a large
More informationDetecting and Parsing of Visual Objects: Humans and Animals. Alan Yuille (UCLA)
Detecting and Parsing of Visual Objects: Humans and Animals Alan Yuille (UCLA) Summary This talk describes recent work on detection and parsing visual objects. The methods represent objects in terms of
More informationarxiv: v3 [cs.cv] 22 Feb 2018
A Closed-form Solution to Photorealistic Image Stylization Yijun Li 1, Ming-Yu Liu 2, Xueting Li 1, Ming-Hsuan Yang 1,2, and Jan Kautz 2 1 University of California, Merced 2 NVIDIA {yli62,xli75,mhyang}@ucmerced.edu
More informationUrban Scene Segmentation, Recognition and Remodeling. Part III. Jinglu Wang 11/24/2016 ACCV 2016 TUTORIAL
Part III Jinglu Wang Urban Scene Segmentation, Recognition and Remodeling 102 Outline Introduction Related work Approaches Conclusion and future work o o - - ) 11/7/16 103 Introduction Motivation Motivation
More informationDecomposing a Scene into Geometric and Semantically Consistent Regions
Decomposing a Scene into Geometric and Semantically Consistent Regions Stephen Gould sgould@stanford.edu Richard Fulton rafulton@cs.stanford.edu Daphne Koller koller@cs.stanford.edu IEEE International
More informationA Closed-form Solution to Photorealistic Image Stylization
A Closed-form Solution to Photorealistic Image Stylization Yijun Li 1, Ming-Yu Liu 2, Xueting Li 1, Ming-Hsuan Yang 1,2, Jan Kautz 2 1 University of California, Merced 2 NVIDIA {yli62,xli75,mhyang}@ucmerced.edu
More information345 Park Ave,, San Jose, CA Homepage:
Jimei Yang Contact Information Research Interests Working Experience 345 Park Ave,, San Jose, CA 95110 E-mail: jimyang@adobe.com Homepage: https://eng.ucmerced.edu/people/jyang44 Deep Learning, Computer
More informationDeep Colorization. arxiv: v1 [cs.cv] 30 Apr Zezhou Cheng, Student Member, IEEE, Qingxiong Yang, Member, IEEE, Bin Sheng, Member, IEEE,
1 Deep Colorization Zezhou Cheng, Student Member, IEEE, Qingxiong Yang, Member, IEEE, Bin Sheng, Member, IEEE, http://www.cs.cityu.edu.hk/ qiyang/publications/iccv15/ arxiv:1605.00075v1 [cs.cv] 30 Apr
More informationSemantic Segmentation
Semantic Segmentation UCLA:https://goo.gl/images/I0VTi2 OUTLINE Semantic Segmentation Why? Paper to talk about: Fully Convolutional Networks for Semantic Segmentation. J. Long, E. Shelhamer, and T. Darrell,
More informationWhere and Who? Automatic Semantic-Aware Person Composition
Where and Who? Automatic Semantic-Aware Person Composition Fuwen Tan 1, Crispin Bernier 1, Benjamin Cohen 1, Vicente Ordonez 1, and Connelly Barnes 1,2 1 University of Virginia, 2 Adobe Research Abstract
More informationUnsupervised Patch-based Context from Millions of Images
Carnegie Mellon University Research Showcase @ CMU Robotics Institute School of Computer Science 12-2011 Unsupervised Patch-based Context from Millions of Images Santosh K. Divvala Carnegie Mellon University
More informationDiscrete Optimization of Ray Potentials for Semantic 3D Reconstruction
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction Marc Pollefeys Joined work with Nikolay Savinov, Christian Haene, Lubor Ladicky 2 Comparison to Volumetric Fusion Higher-order ray
More informationA Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes
A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes Qixing Huang Stanford University huangqx@stanford.edu Mei Han Google Inc. meihan@google.com Bo Wu Google
More informationWhere and Who? Automatic Semantic-Aware Person Composition
Where and Who? Automatic Semantic-Aware Person Composition Fuwen Tan University of Virginia Vicente Ordonez University of Virginia Crispin Bernier University of Virginia Connelly Barnes University of Virginia
More informationCombining Semantic Scene Priors and Haze Removal for Single Image Depth Estimation
Combining Semantic Scene Priors and Haze Removal for Single Image Depth Estimation Ke Wang Enrique Dunn Joseph Tighe Jan-Michael Frahm University of North Carolina at Chapel Hill Chapel Hill, NC, USA {kewang,dunn,jtighe,jmf}@cs.unc.edu
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 informationContext-Based Automatic Local Image Enhancement
Context-Based Automatic Local Image Enhancement Sung Ju Hwang 1, Ashish Kapoor 2, and Sing Bing Kang 2 1 The University of Texas, Austin,TX, USA sjhwang@cs.utexas.edu 2 Microsoft Research, Redmond, WA,
More informationFully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation Jonathan Long* Evan Shelhamer* Trevor Darrell UC Berkeley Chaim Ginzburg for Deep Learning seminar 1 Semantic Segmentation Define a pixel-wise labeling
More informationDense Image Labeling Using Deep Convolutional Neural Networks
Dense Image Labeling Using Deep Convolutional Neural Networks Md Amirul Islam, Neil Bruce, Yang Wang Department of Computer Science University of Manitoba Winnipeg, MB {amirul, bruce, ywang}@cs.umanitoba.ca
More informationPartial Least Squares Regression on Grassmannian Manifold for Emotion Recognition
Emotion Recognition In The Wild Challenge and Workshop (EmotiW 2013) Partial Least Squares Regression on Grassmannian Manifold for Emotion Recognition Mengyi Liu, Ruiping Wang, Zhiwu Huang, Shiguang Shan,
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 informationRushes Video Segmentation Using Semantic Features
Rushes Video Segmentation Using Semantic Features Athina Pappa, Vasileios Chasanis, and Antonis Ioannidis Department of Computer Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece
More informationLearning Hierarchical Features for Scene Labeling
Learning Hierarchical Features for Scene Labeling FB Informatik Knowledge Engineering Group Prof. Dr. Johannes Fürnkranz Seminar Machine Learning Author : Tanya Harizanova 14.01.14 Seminar aus maschinellem
More informationAmodal and Panoptic Segmentation. Stephanie Liu, Andrew Zhou
Amodal and Panoptic Segmentation Stephanie Liu, Andrew Zhou This lecture: 1. 2. 3. 4. Semantic Amodal Segmentation Cityscapes Dataset ADE20K Dataset Panoptic Segmentation Semantic Amodal Segmentation Yan
More informationMulti-Human Parsing Machines
Multi-Human Parsing Machines Jianshu Li 1,3 Jian Zhao 2 Yunpeng Chen 2 Sujoy Roy 3 Shuicheng Yan 2 Jiashi Feng 2 Terence Sim 1 1 School of Computing, National University of Singapore 2 Electrical & Computer
More informationDeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution and Fully Connected CRFs Zhipeng Yan, Moyuan Huang, Hao Jiang 5/1/2017 1 Outline Background semantic segmentation Objective,
More informationCourse Administration
Course Administration Project 2 results are online Project 3 is out today The first quiz is a week from today (don t panic!) Covers all material up to the quiz Emphasizes lecture material NOT project topics
More informationOpportunities of Scale
Opportunities of Scale 11/07/17 Computational Photography Derek Hoiem, University of Illinois Most slides from Alyosha Efros Graphic from Antonio Torralba Today s class Opportunities of Scale: Data-driven
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 informationKalyan Sunkavalli EDUCATION WORK EXPERIENCE PUBLICATIONS. ET07-128, 321 Park Ave
Kalyan Sunkavalli ET07-128, 321 Park Ave San Jose, CA 95110 http://www.eecs.harvard.edu/~kalyans sunkaval@adobe.com EDUCATION Harvard University, Cambridge, MA Ph.D., Computer Science, May 2012 Advisor:
More informationAutomatic Trimap Generation for Digital Image Matting
Automatic Trimap Generation for Digital Image Matting Chang-Lin Hsieh and Ming-Sui Lee Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, R.O.C. E-mail:
More informationTopics to be Covered in the Rest of the Semester. CSci 4968 and 6270 Computational Vision Lecture 15 Overview of Remainder of the Semester
Topics to be Covered in the Rest of the Semester CSci 4968 and 6270 Computational Vision Lecture 15 Overview of Remainder of the Semester Charles Stewart Department of Computer Science Rensselaer Polytechnic
More informationClosed-Loop Adaptation for Robust Tracking
Closed-Loop Adaptation for Robust Tracking Jialue Fan, Xiaohui Shen, and Ying Wu Northwestern University 2145 Sheridan Road, Evanston, IL 60208 {jfa699,xsh835,yingwu}@eecs.northwestern.edu Abstract. Model
More informationAnalysis: TextonBoost and Semantic Texton Forests. Daniel Munoz Februrary 9, 2009
Analysis: TextonBoost and Semantic Texton Forests Daniel Munoz 16-721 Februrary 9, 2009 Papers [shotton-eccv-06] J. Shotton, J. Winn, C. Rother, A. Criminisi, TextonBoost: Joint Appearance, Shape and Context
More informationSuperParsing: Scalable Nonparametric Image Parsing with Superpixels
SuperParsing: Scalable Nonparametric Image Parsing with Superpixels Joseph Tighe and Svetlana Lazebnik Dept. of Computer Science, University of North Carolina at Chapel Hill Chapel Hill, NC 27599-3175
More informationContexts and 3D Scenes
Contexts and 3D Scenes Computer Vision Jia-Bin Huang, Virginia Tech Many slides from D. Hoiem Administrative stuffs Final project presentation Nov 30 th 3:30 PM 4:45 PM Grading Three senior graders (30%)
More informationECS 289H: Visual Recognition Fall Yong Jae Lee Department of Computer Science
ECS 289H: Visual Recognition Fall 2014 Yong Jae Lee Department of Computer Science Plan for today Questions? Research overview Standard supervised visual learning building Category models Annotators tree
More informationObject Recognition. Computer Vision. Slides from Lana Lazebnik, Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce
Object Recognition Computer Vision Slides from Lana Lazebnik, Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce How many visual object categories are there? Biederman 1987 ANIMALS PLANTS OBJECTS
More informationRich feature hierarchies for accurate object detection and semantic segmentation
Rich feature hierarchies for accurate object detection and semantic segmentation BY; ROSS GIRSHICK, JEFF DONAHUE, TREVOR DARRELL AND JITENDRA MALIK PRESENTER; MUHAMMAD OSAMA Object detection vs. classification
More informationSemantic Object Parsing with Local-Global Long Short-Term Memory
Semantic Object Parsing with Local-Global Long Short-Term Memory Xiaodan Liang 1,3, Xiaohui Shen 4, Donglai Xiang 3, Jiashi Feng 3 Liang Lin 1, Shuicheng Yan 2,3 1 Sun Yat-sen University 2 360 AI Institute
More informationObject recognition (part 2)
Object recognition (part 2) CSE P 576 Larry Zitnick (larryz@microsoft.com) 1 2 3 Support Vector Machines Modified from the slides by Dr. Andrew W. Moore http://www.cs.cmu.edu/~awm/tutorials Linear Classifiers
More informationFoveaNet: Perspective-aware Urban Scene Parsing
FoveaNet: Perspective-aware Urban Scene Parsing Xin Li 1,2 Zequn Jie 3 Wei Wang 4,2 Changsong Liu 1 Jimei Yang 5 Xiaohui Shen 5 Zhe Lin 5 Qiang Chen 6 Shuicheng Yan 2,6 Jiashi Feng 2 1 Department of EE,
More informationIMA Preprint Series # 2153
DISTANCECUT: INTERACTIVE REAL-TIME SEGMENTATION AND MATTING OF IMAGES AND VIDEOS By Xue Bai and Guillermo Sapiro IMA Preprint Series # 2153 ( January 2007 ) INSTITUTE FOR MATHEMATICS AND ITS APPLICATIONS
More informationDeblurring Text Images via L 0 -Regularized Intensity and Gradient Prior
Deblurring Text Images via L -Regularized Intensity and Gradient Prior Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang School of Mathematical Sciences, Dalian University of Technology Electrical Engineering
More informationBag-of-features. Cordelia Schmid
Bag-of-features for category classification Cordelia Schmid Visual search Particular objects and scenes, large databases Category recognition Image classification: assigning a class label to the image
More informationWhen Big Datasets are Not Enough: The need for visual virtual worlds.
When Big Datasets are Not Enough: The need for visual virtual worlds. Alan Yuille Bloomberg Distinguished Professor Departments of Cognitive Science and Computer Science Johns Hopkins University Computational
More informationFashion Analytics and Systems
Learning and Vision Group, NUS (NUS-LV) Fashion Analytics and Systems Shuicheng YAN eleyans@nus.edu.sg National University of Singapore [ Special thanks to Luoqi LIU, Xiaodan LIANG, Si LIU, Jianshu LI]
More informationDiscriminative classifiers for image recognition
Discriminative classifiers for image recognition May 26 th, 2015 Yong Jae Lee UC Davis Outline Last time: window-based generic object detection basic pipeline face detection with boosting as case study
More informationLEARNING BOUNDARIES WITH COLOR AND DEPTH. Zhaoyin Jia, Andrew Gallagher, Tsuhan Chen
LEARNING BOUNDARIES WITH COLOR AND DEPTH Zhaoyin Jia, Andrew Gallagher, Tsuhan Chen School of Electrical and Computer Engineering, Cornell University ABSTRACT To enable high-level understanding of a scene,
More informationCS4670 / 5670: Computer Vision Noah Snavely
{ { 11/26/2013 CS4670 / 5670: Computer Vision Noah Snavely Graph-Based Image Segmentation Stereo as a minimization problem match cost Want each pixel to find a good match in the other image smoothness
More informationA New Technique for Adding Scribbles in Video Matting
www.ijcsi.org 116 A New Technique for Adding Scribbles in Video Matting Neven Galal El Gamal 1, F. E.Z. Abou-Chadi 2 and Hossam El-Din Moustafa 3 1,2,3 Department of Electronics & Communications Engineering
More informationShape Preserving RGB-D Depth Map Restoration
Shape Preserving RGB-D Depth Map Restoration Wei Liu 1, Haoyang Xue 1, Yun Gu 1, Qiang Wu 2, Jie Yang 1, and Nikola Kasabov 3 1 The Key Laboratory of Ministry of Education for System Control and Information
More informationRON: Reverse Connection with Objectness Prior Networks for Object Detection
RON: Reverse Connection with Objectness Prior Networks for Object Detection Tao Kong 1, Fuchun Sun 1, Anbang Yao 2, Huaping Liu 1, Ming Lu 3, Yurong Chen 2 1 Department of CST, Tsinghua University, 2 Intel
More informationLearning to Segment Instances in Videos with Spatial Propagation Network
The 2017 DAVIS Challenge on Video Object Segmentation - CVPR 2017 Workshops Learning to Segment Instances in Videos with Spatial Propagation Network Jingchun Cheng 1,2 Sifei Liu 2 Yi-Hsuan Tsai 2 Wei-Chih
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 information3D Shape Analysis with Multi-view Convolutional Networks. Evangelos Kalogerakis
3D Shape Analysis with Multi-view Convolutional Networks Evangelos Kalogerakis 3D model repositories [3D Warehouse - video] 3D geometry acquisition [KinectFusion - video] 3D shapes come in various flavors
More informationarxiv: v2 [cs.cv] 16 Sep 2018
Dual Attention Network for Scene Segmentation Jun Fu, Jing Liu, Haijie Tian, Zhiwei Fang, Hanqing Lu CASIA IVA {jun.fu, jliu, zhiwei.fang, luhq}@nlpr.ia.ac.cn,{hjtian bit}@63.com arxiv:809.0983v [cs.cv]
More informationSupervised learning. y = f(x) function
Supervised learning y = f(x) output prediction function Image feature Training: given a training set of labeled examples {(x 1,y 1 ),, (x N,y N )}, estimate the prediction function f by minimizing the
More informationA Few Things to Know about Machine Learning for Web Search
AIRS 2012 Tianjin, China Dec. 19, 2012 A Few Things to Know about Machine Learning for Web Search Hang Li Noah s Ark Lab Huawei Technologies Talk Outline My projects at MSRA Some conclusions from our research
More informationPart-based and local feature models for generic object recognition
Part-based and local feature models for generic object recognition May 28 th, 2015 Yong Jae Lee UC Davis Announcements PS2 grades up on SmartSite PS2 stats: Mean: 80.15 Standard Dev: 22.77 Vote on piazza
More informationDEPT: Depth Estimation by Parameter Transfer for Single Still Images
DEPT: Depth Estimation by Parameter Transfer for Single Still Images Xiu Li 1, 2, Hongwei Qin 1,2, Yangang Wang 3, Yongbing Zhang 1,2, and Qionghai Dai 1 1. Dept. of Automation, Tsinghua University 2.
More informationWhat are we trying to achieve? Why are we doing this? What do we learn from past history? What will we talk about today?
Introduction What are we trying to achieve? Why are we doing this? What do we learn from past history? What will we talk about today? What are we trying to achieve? Example from Scott Satkin 3D interpretation
More informationJoint Inference in Image Databases via Dense Correspondence. Michael Rubinstein MIT CSAIL (while interning at Microsoft Research)
Joint Inference in Image Databases via Dense Correspondence Michael Rubinstein MIT CSAIL (while interning at Microsoft Research) My work Throughout the year (and my PhD thesis): Temporal Video Analysis
More informationHIERARCHICAL JOINT-GUIDED NETWORKS FOR SEMANTIC IMAGE SEGMENTATION
HIERARCHICAL JOINT-GUIDED NETWORKS FOR SEMANTIC IMAGE SEGMENTATION Chien-Yao Wang, Jyun-Hong Li, Seksan Mathulaprangsan, Chin-Chin Chiang, and Jia-Ching Wang Department of Computer Science and Information
More informationSynthesis of Near-regular Natural Textures
Synthesis of Near-regular Natural Textures V. Asha Dept. of Master of Computer Applications New Horizon College of Engineering Bangalore, Karnataka, INDIA asha.gurudath@yahoo.com Abstract : Texture synthesis
More informationDeep Colorization. Qingxiong Yang City University of Hong Kong Zezhou Cheng Shanghai Jiao Tong University
Deep Colorization Zezhou Cheng Shanghai Jiao Tong University chengzezhou@sjtu.edu.cn Qingxiong Yang City University of Hong Kong qiyang@cityu.edu.hk Bin Sheng Shanghai Jiao Tong University shengbin@sjtu.edu.cn
More informationarxiv: v2 [cs.cv] 30 Sep 2018
A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images arxiv:1809.03917v2 [cs.cv] 30 Sep 2018 Chengyao Qian, Ting Liu, Hao Jiang, Zhe Wang, Pengfei Wang, Mingxin Guan
More informationAutomatic Colorization of Grayscale Images
Automatic Colorization of Grayscale Images Austin Sousa Rasoul Kabirzadeh Patrick Blaes Department of Electrical Engineering, Stanford University 1 Introduction ere exists a wealth of photographic images,
More informationAutomatically Identifying and Georeferencing Street Maps on the Web
Automatically Identifying and Georeferencing Street Maps on the Web Sneha Desai, Craig A. Knoblock, Yao-Yi Yi Chiang, Ching-Chien Chien Chen and Kandarp Desai University of Southern California Department
More informationREJECTION-BASED CLASSIFICATION FOR ACTION RECOGNITION USING A SPATIO-TEMPORAL DICTIONARY. Stefen Chan Wai Tim, Michele Rombaut, Denis Pellerin
REJECTION-BASED CLASSIFICATION FOR ACTION RECOGNITION USING A SPATIO-TEMPORAL DICTIONARY Stefen Chan Wai Tim, Michele Rombaut, Denis Pellerin Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France ABSTRACT
More informationRecognition of Gurmukhi Text from Sign Board Images Captured from Mobile Camera
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 17 (2014), pp. 1839-1845 International Research Publications House http://www. irphouse.com Recognition of
More informationMartian lava field, NASA, Wikipedia
Martian lava field, NASA, Wikipedia Old Man of the Mountain, Franconia, New Hampshire Pareidolia http://smrt.ccel.ca/203/2/6/pareidolia/ Reddit for more : ) https://www.reddit.com/r/pareidolia/top/ Pareidolia
More informationSpatially Constrained Location Prior for Scene Parsing
Spatially Constrained Location Prior for Scene Parsing Ligang Zhang, Brijesh Verma, David Stockwell, Sujan Chowdhury Centre for Intelligent Systems School of Engineering and Technology, Central Queensland
More informationColor Adjustment for Seamless Cloning based on Laplacian-Membrane Modulation
Color Adjustment for Seamless Cloning based on Laplacian-Membrane Modulation Bernardo Henz, Frederico A. Limberger, Manuel M. Oliveira Instituto de Informática UFRGS Porto Alegre, Brazil {bhenz,falimberger,oliveira}@inf.ufrgs.br
More informationClass 9 Action Recognition
Class 9 Action Recognition Liangliang Cao, April 4, 2013 EECS 6890 Topics in Information Processing Spring 2013, Columbia University http://rogerioferis.com/visualrecognitionandsearch Visual Recognition
More informationDHTC: An Effective DXTC-based HDR Texture Compression Scheme
DHTC: An Effective DXTC-based HDR Texture Compression Scheme Wen Sun 1,2 Yan Lu 1 Feng Wu 1 Shipeng Li 1 yanlu@microsoft.com 1 Microsoft Research Asia 2 University of Science and Technology of China Outline
More informationUnsupervised 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 informationTexture. CS 419 Slides by Ali Farhadi
Texture CS 419 Slides by Ali Farhadi What is a Texture? Texture Spectrum Steven Li, James Hays, Chenyu Wu, Vivek Kwatra, and Yanxi Liu, CVPR 06 Texture scandals!! Two crucial algorithmic points Nearest
More informationLAYOUT-EXPECTATION-BASED MODEL FOR IMAGE SEARCH RE-RANKING
LAYOUT-EXPECTATION-BASED MODEL FOR IMAGE SEARCH RE-RANKING Bin Jin 1, Weiyao Lin 1, Jianxin Wu, Tianhao Wu 1, Jun Huang 1, Chongyang Zhang 1 1 Department of Electronic Engineering, School of Computer Engineering,
More informationAutomatic Video Caption Detection and Extraction in the DCT Compressed Domain
Automatic Video Caption Detection and Extraction in the DCT Compressed Domain Chin-Fu Tsao 1, Yu-Hao Chen 1, Jin-Hau Kuo 1, Chia-wei Lin 1, and Ja-Ling Wu 1,2 1 Communication and Multimedia Laboratory,
More informationDynamic Shape Tracking via Region Matching
Dynamic Shape Tracking via Region Matching Ganesh Sundaramoorthi Asst. Professor of EE and AMCS KAUST (Joint work with Yanchao Yang) The Problem: Shape Tracking Given: exact object segmentation in frame1
More informationRegion-based Segmentation and Object Detection
Region-based Segmentation and Object Detection Stephen Gould Tianshi Gao Daphne Koller Presented at NIPS 2009 Discussion and Slides by Eric Wang April 23, 2010 Outline Introduction Model Overview Model
More informationCost-alleviative Learning for Deep Convolutional Neural Network-based Facial Part Labeling
[DOI: 10.2197/ipsjtcva.7.99] Express Paper Cost-alleviative Learning for Deep Convolutional Neural Network-based Facial Part Labeling Takayoshi Yamashita 1,a) Takaya Nakamura 1 Hiroshi Fukui 1,b) Yuji
More informationComputer Vision: Making machines see
Computer Vision: Making machines see Roberto Cipolla Department of Engineering http://www.eng.cam.ac.uk/~cipolla/people.html http://www.toshiba.eu/eu/cambridge-research- Laboratory/ Vision: what is where
More informationarxiv: v1 [cs.cv] 11 Aug 2017
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel arxiv:1708.03423v1 [cs.cv] 11 Aug 2017 Wenqi Ren 1,2, Jinshan Pan 3, Xiaochun Cao 1,4, and Ming-Hsuan Yang 5 1 State Key Laboratory
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 informationBlind Image Deblurring Using Dark Channel Prior
Blind Image Deblurring Using Dark Channel Prior Jinshan Pan 1,2,3, Deqing Sun 2,4, Hanspeter Pfister 2, and Ming-Hsuan Yang 3 1 Dalian University of Technology 2 Harvard University 3 UC Merced 4 NVIDIA
More information