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People New Hires Jiang BIAN, Yahoo <strong>Research</strong> Karlsson BORJE, Nokia Xun GUO, MediaTek Kwantaek KIM, LG Zhuang LI, MSR SVC Xin LIU, UC Davis Tim PAN, GEMFOR Tech Takaaki SHIRATORI, CMU/Disney Hong TAN, Purdue – Haptics & HCI Yingcai WU, UC Davis Oversea Ph.D’s Liang CHEN (UC San Diego), Koji YATANI(Univ. of Toronto), 15 domestic Ph.D’s & Masters
People and <strong>Research</strong> Areas Hsiao-Wuen HON HCI Hong TAN Speech Frank SOONG Innovation Engineering Jonathan TIEN Technology Strategy Eric CHANG Baining GUO Visual Computing - Yi MA Graphics - Baining GUO Multimedia - Shipeng LI, Feng WU Theory - Baining GUO Wei-Ying MA Web/Data Search & Mining – Jirong WEN, Haixun WANG Natural Language Processing – Ming ZHOU Multimedia Search & Mining – Lei ZHANG Knowledge Mining – Yong RUI, Chin-Yew LIN Internet Economics – Tie-Yan LIU Machine Learning – Zheng CHEN Feng ZHAO System - Zheng ZHANG, Lidong ZHOU Network & Wireless - Yongguang ZHANG Hardware - Feng-Hsiung HSU Software Analytics - Dongmei ZHANG Mobile & Sensing – Feng ZHAO
Page 1 and 2: Microsoft Research Asia Update Hsia
Page 3: Microsoft Research Research lab loc
Page 7 and 8: Research Groups Hardware Computing
Page 9 and 10: Research Updates Natural User Inter
Page 11 and 12: Kinect based Object Digitization Si
Page 13 and 14: Comparisons 35,862 Triangles 36MB M
Page 15 and 16: Avatar Kinect Facial expression tra
Page 17 and 18: Xbox Kinect Pipeline: Skeleton Corr
Page 19 and 20: Skeleton/Gesture Alignment •The s
Page 21 and 22: Output Distribution Model Random fo
Page 23 and 24: Qualitative Results
Page 25 and 26: Kinect Based 3D Face Avatar 3D face
Page 27 and 28: Kinect Based 3D Face Avatar (Fang Y
Page 29 and 30: Goal: 3D Hair from Single Image Sin
Page 31 and 32: Result Single portrait image + user
Page 33 and 34: Strand Tracing
Page 35 and 36: 3D Strands: hair volume Additional
Page 37 and 38: Portrait Pop-ups: layers completion
Page 39 and 40: Hairstyle Replacement Build pop-up
Page 41 and 42: Hairstyle Replacement: more results
Page 43 and 44: Limitations Not a complete 3D model
Page 45 and 46: 3D Hair Replacement
Page 47 and 48: Research Updates Search and online
Page 49 and 50: What Are Attributes? Clipart Linedr
Page 51 and 52: What Are Attributes? Simple Backgro
Page 53 and 54: Color/Clipart/Linedrawing Filter
Page 55 and 56:
Color Filter Examples dog + red Red
Page 57 and 58:
Color Filter Examples sunset + blue
Page 59 and 60:
Intention Analysis (w/o Query Objec
Page 61 and 62:
Original Images
Page 63 and 64:
Our Solution • Salient object det
Page 65 and 66:
Our Solution • Salient object det
Page 67 and 68:
BMW + blue
Page 69 and 70:
BMW+ red
Page 71 and 72:
dog
Page 73 and 74:
dog + blue
Page 75 and 76:
dog + black
Page 77 and 78:
forest
Page 79 and 80:
forest + red
Page 81 and 82:
sunset
Page 83 and 84:
sunset + green
Page 85 and 86:
tulips
Page 87 and 88:
tulips + purple
Page 89 and 90:
Taylor Swift
Page 91 and 92:
Taylor Swift + red
Page 93 and 94:
Important Filters Usage on image se
Page 95 and 96:
Cascaded Filtering Simple Backgroun
Page 97 and 98:
Cascaded Filtering Web Images Backg
Page 99 and 100:
Cascaded Filtering Shape Filter: Pr
Page 101 and 102:
Cascaded Filtering Texture Filter:
Page 103 and 104:
Page 105 and 106:
Cascaded Filtering Photo Filter: Cl
Page 107 and 108:
Cascaded Filtering Text Filter: Tex
Page 109 and 110:
Page 111 and 112:
Relevance ≠ Clipart Relevance Ran
Page 113 and 114:
Results without Clipart Specific Te
Page 115 and 116:
Results without Clipart Specific Te
Page 117 and 118:
Bing Clipart Filter • Visual filt
Page 119:
Results
Page 124 and 125:
Cascaded Filtering Text Filter: Fil
Page 126 and 127:
Motivation Visual quality is import
Page 128 and 129:
Integrate Visual Features Color- Hi
Page 130 and 131:
Image Quality Related Attributes Pe
Page 132 and 133:
Image Quality Related Attributes Ob
Page 134 and 135:
Image Quality Related Attributes Im
Page 136 and 137:
Image Quality Related Attributes Ge
Page 138 and 139:
Comparison Results Old results (que
Page 140 and 141:
Comparison Results Old results (que
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Research Updates Data Intensive Com
Page 144 and 145:
Urban Computing Improving Sensing U
Page 146 and 147:
1. GPS trajectories of 33,000 taxis
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3. Ticketing data of public transpo
Page 150 and 151:
5. Real estate market
Page 152 and 153:
7. POI Data (2007 - 2012)
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Discover Regions of Different Funct
Page 156 and 157:
Applications • Calibrating urban
Page 158 and 159:
Methodology Overview Mapping from r
Page 160 and 161:
Annotation of Territories Emerging
Page 162 and 163:
Research Updates CS Fundamentals Th
Page 164 and 165:
GPUs vs. Programmers GPU architectu
Page 166 and 167:
Research on BSGP (2007~now) Compile
Page 168 and 169:
Contribution to Community BSGP down
Page 170 and 171:
Motivation Growing demand for fineg
Page 172 and 173:
Key Problem How to efficiently buil
Page 174 and 175:
Enabling Walking-based Collection I
Page 176 and 177:
Path/Trace list # of Effective turn
Page 178 and 179:
End-to-end Evaluation* 100% 95% 90%
Page 180 and 181:
An Illustrative Example Start Actua
Page 182 and 183:
Conclusion MSRA follows the same mi
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