20180314 at National Taiwan Normal University. Reflection on my own career from being inspired to work on CV/ML research during my graduate studies at NTNU, then going abroad to obtain my Ph.D. and later on my career in this field. The talk emphasizes on the importance of innovation and how to realize ones new ideas within large and small organizations.

1. Find your passion and make a
difference in your career!
Albert Y. C. Chen, Ph.D.
Vice President, R&D
Viscovery

2. Albert Y. C. Chen, Ph.D.
陳彥呈博⼠士
albert@viscovery.com
http://slideshare.net/albertycchen
• Experience
2017-present: Vice President of R&D @ Viscovery
2015-2017: Chief Scientist @ Viscovery
2015-2015: Principal Scientist @ Nervve Technologies
2013-2014 Senior Scientist @ Tandent Vision Science
2011-2012 @ GE Global Research, Computer Vision Lab
• Education
Ph.D. in Computer Science, SUNY-Buffalo
M.S. in Computer Science, NTNU
B.S. in Computer Science, NTHU

3. • Face it. You'll be working the majority of your
time, for the rest of your life.
• Do something meaningful, do something you are
passionate about.
Life
24 36 48 60 72

4. • What are the things that you can't wait to get up
every morning and do it all over again?
What are you passionate about?
I've got a dream!

5. My crush on CV, HCI, MM, ML...
Can't wait to figure out the world!
Freestyle Sketching Stage
AirTouch waits in background
for the initialization signal
Initialize
Terminate
Output
image
database
Start:
Results
CBIR
query
Airtouch HCI interface for Content-based Image Retrieval

6. Interactive Segmentation & Classification
• Segmentation then classification:
• computationally more efficient,
• results in much higher classification accuracy.
• Pioneered the “pixel label propagation” field.
• First to utilize superpixels and supervoxels for the task.
FG
Traditional Spatial
Propagation
Pixel label map
Label a subset of pixels
BG
Spatio-temporal Propagation
time

7. Image/Video Object Recognition
and Content Understanding
approaches
person carries
gives
recieves
Ontology
object
Person 1
Person 1Person 2
High-Level
Mid-Level
approach
activity
receives gives
carries
activity
activity activity
Time
Reasoning
x
x
x
Low-Level
x x
x
x

8. Learning and Adapting Optimal
Classifier Parameters
subspace B
subspace
A
subspace
C
Image-level feature space
priors
Patch-level feature space
posterior
probability
suggest optimal
parameter configuration

9. Graphical Models and
Stochastic Optimization
A
(a) The space-time volume of a
video showing the objects
(A--F) and their appearing
time-span.
space
time
A
B
C
D
E
F
B E
F
C
D
(b) The temporal relationship
graph. An edge between
two vertices mean that the
two objects overlap in time.
(c) The goal is: cover all objects
with the smallest number of
"ground truth key frames".
space
time
A
B
C
D
E
F
key 1 key 2
A
B E
F
C
D
(d) This translates to: iteratively
solving the max clique
problem until all vertices
belong to a clique.
A
B E
F
C
D
key 2
key 1
frame t-1 frame t
layer n layer n
layer n+1 layer n+1
Temporal
Shift
Shift
µ

10. Medical Imaging and
Geospatial Imaging
GNN detection and
segmentation
in Lung CT geospatial imaging:
building detection
Brain tumor detection and
segmentation in MR images.

11. 1. W.Wu,A.Y. C. Chen, L. Zhao, and J. J. Corso. Brain tumor detection and segmentation in a CRF
framework with pixel-wise affinity and superpixel-level features. International Journal of Computer
Assisted Radiology and Surgery, 2015.
2. S. N. Lim,A.Y. C. Chen and X.Yang. Parameter Inference Engine (PIE) on the Pareto Front. In
Proceedings of International Conference of Machine Learning,Auto ML Workshop, 2014.
3. A.Y.C. Chen and J.J. Corso.Temporally consistent multi-class video-object segmentation with the
video graph-shifts algorithm. In Proceedings of IEEE Workshop on Applications of ComputerVision,
2011.
4. D.R. Schlegel,A.Y.C. Chen, C. Xiong, J.A. Delmerico, and J.J. Corso. Airtouch: Interacting with
computer systems at a distance. In Proceedings of IEEE Workshop on Applications of Computer
Vision, 2011.
5. A.Y.C. Chen and J.J. Corso. On the effects of normalization in adaptive MRF Hierarchies. In
Proceedings of International Symposium CompIMAGE, 2010.
6. A.Y.C. Chen and J.J. Corso. Propagating multi-class pixel labels throughout video frames. In
Proceedings of IEEE Western NewYork Image Processing Workshop, 2010.
7. A.Y. C. Chen and J. J. Corso. On the effects of normalization in adaptive MRF Hierarchies.
Computational Modeling of Objects Represented in Images, pages 275–286, 2010.
8. Y.Tao, L. Lu, M. Dewan,A.Y. C. Chen, J. J. Corso, J. Xuan, M. Salganicoff, and A. Krishnan. Multi-level
ground glass nodule detection and segmentation in ct lung images. Medical Image Computing and
Computer-Assisted Intervention, 2009.
9. A.Y.C. Chen, J.J. Corso, and L.Wang. Hops: Efficient region labeling using higher order proxy
neighborhoods. In Proceedings of IEEE International Conference on Pattern Recognition, 2008.

12. • I gained the following capabilities throughout my
graduate studies:
• problem solving skills (research/engineering),
• the art of teaching,
• entrepreneurship.
• Furthermore, I'm really into my field of study, AI.
• I asked myself, how can I balance between
these things for my career?
Upon getting my Ph.D.

13. • Realized being Principal Investigator (PI) is not
that much different from being an entrepreneur!
• Entrepreneur has more control over where the
money is spent, how quickly it is spent, and get
more out of fruit of hard labor!
• Also, I get to do RD, BD, and mentoring, all at
once!
• However, I still have many things to learn before
I fly solo.
Inflection point

14. • How century-old multi-national corporation
remain competitive by reinventing itself.
• How wave after wave of innovations become the
new pillars of the US economy.
• How Taiwan failed to gain significance in the
Internet-based economy in the past 20 years.
• How I might be able to help make a difference
with my limited capability, in my limited lifetime.
Throughout the years, I observed...

15. Why Risk Innovating?
• Good business model NEVER last forever.
• Average “shelf life” on S&P 500: 20 years.
• 100-year old companies constantly reinvent
themselves every 10-20 years
• Startups contribute to 20% of USA’s GDP.

16. Change is the only constant
-Heraclitus (535 BC - 475 BC)

17. Change is the only constant
-Heraclitus (535 BC - 475 BC)

18. The Death of a Good
Business Model
• Foxconn 20 year revenue v.s. net profit (now at 5%)

19. How 100 year old corporations
remain innovative & competitive
GE Schenectady, 1896

20. History of change at GE
• 1886: one of the 12 original companies on the Dow
Jone Industrial Average (also the only one remaining).
• 1889: lightbulbs
• 1919: radios
• 1927: TV
• 1941: jet engine
• 1960: nuclear power
• 1971: room AC units
• 1995: MRI

21. History of change at IBM
• 1960s: mainframe computer
• 1980s: personal computer
• 2000s: integrated solutions
• 2020s: AI, Watson

22. How about the leading
Semiconductor companies?

23. NVidia reinventing itself
—2 times in 20 years

24. “Bad money drives out good”
in the desktop GPU market

25. The rise of mobile computing,
and how NVidia missed the boat!

26. NVidia’s Tegra mobile
processors never took off
then, the market
saturated…

27. NVidia not just survived.
NVidia is thriving!

28. Meet the new NVidia: Deep Learning,
Deep Learning, and still, Deep Learning

30. The king is dead,
long live the king!

31. Why startups have to be
innovative to stay competitive
• Startup: a company in pursuit of a bold new
business model that can scale.

32. Must take risks to gain edge, being small

33. The Venture Capital Funnel is
extremely efficient at filtering ideas

34. Don't romanticize startup life;
it's a cruel struggle for survival...

35. Before making the leap,
you must ask yourself
• Are you passionate about your idea?
• Is your idea disruptive enough?
• What is your business plan?
• What is it?
• Can it make money?
• What is the future of the idea?
• What is your competitive advantage?
• How do you build up your entry barrier?

36. Furthermore, be prepared to face
constant, repeated rejections
NO
Ha Ha
Ha
NO
NO

37. It doesn't really matter
how you start

38. nor does it matter
when you start

39. It's important though, to
fail early, fail often, fail forward!
Build
Measure
Learn
The lean cycle
FAIL?
Ideate
pivot leap of faith
MVP
Build
Measure
Learn
Start all over again!
MVP

40. It's also important to quickly
build up your entry barrier!
• Speed
• Team
• Technology
• Market (users)
niche, niche, and niche!

41. The Goldilocks zone of innovation

42. The Goldilocks zone of innovation
Business
Relevance
Academic
Relevance
plentiful resources; hierarchical organization
lack of resources; responsive organization
traditional corporations
talking “innovation”
corporate research
startups struggling to survive
academic spinoffs
MSR

43. Niche market/technology for AI startups
(BCG AI Report, 2016/10)
appl.
layer
tech
layer
infra
layer
solution
platform
libraries
modules
data
machine computing power
data accumulation via open API
AI/DNN library AI/DNN library
general purpose
platforms
general purpose
platforms
app-specific
platforms
app-specific
platforms
app app app app app
HW
co.
VerticalAIStartups
agri. manu. med. fin. retail trans.
E.g., 1: Google, Amazon, FB, 2: IBM, 3: Walmart, 5: NVidia

44. Vertical AI Startups
Solving industry-specific problems by combining
AI and Subject Matter Expertise.
• Full Stack Products
• Subject Matter Expertise
• Proprietary Data
• AI delivers core value
(Bradford Cross, 2017/06/14)

45. Why Go Vertical?
• Don’t get ripped off—don’t get disconnected
from key customer/providers.
• Tasks get commoditized
• Software is eating the world—every company in
every industry needs to be a tech company.
• Enterprise exits come in cohorts
(Bradford Cross, 2017/06/14)

46. So, which vertical?

47. (Bradford Cross, 2017/06/14)
• choose large TAM
with high margin
• avoid 1% fallacy
• avoid confirmation
bias
So, which vertical?

48. (Bradford Cross, 2017/06/14)
AI startups total investmentUnicorn tally

50. Big
Innovation
Large
Market
Wide
Exit
Beware of the
"Technical Founder's Trap"
cannot
exit
no
edge
another
SME
Easy trap
for technical
founders to
fall into.
Not necessarily
bad for a first step.
Dominate the
small market and
then expand to
neighboring one.

51. Big Innovations with small
markets or narrow exits

52. Image Panoramas
(1980s - now)

53. Solving Panorama Problem
with Markov Random Fields
Input:

54. Solving Panorama Problem
with Markov Random Fields
Input:

55. Solving Panorama Problem
with Markov Random Fields
Input:

56. Solving Panorama Problem
with Markov Random Fields
Input:

57. Solving Panorama Problem
with Markov Random Fields
Input:

58. Solving Panorama Problem
with Markov Random Fields
Input:

59. Solving Panorama Problem
with Markov Random Fields
Input:

60. Solving Panorama Problem
with Markov Random Fields

61. Solving Panorama Problem
with Markov Random Fields
ICM (Iterated Conditional Modes), 1986

62. Solving Panorama Problem
with Markov Random Fields
Belief Propagation (1980-2000)

63. Solving Panorama Problem
with Markov Random Fields
Graph-Cuts (alpha expansion), 2001

64. Photosynthesis

65. Solving Photosynthesis Problems
with Alpha-matting (2000s-now)

66. Self-driving cars (2000s-now)

67. DARPA Grand Challenge
(2005)

68. 2005 winner, Stanley (Stanford),
3mph through desert

70. DARPA Urban Challenge
(2007)

71. 2007 winner, Boss (CMU),
13mpg through the city

72. Self Driving Cadillac, US
congressman to airport, 2013

73. Google Self Driving Car,
2015

74. Google Self-Driving Car,
2016

75. Google Self-Driving Car, 2016

76. NVidia Self Driving Car, 2016

77. How did we come this far?
Race car drivers know the trick

78. Focus on Free Space /
Drivable Area, not Obstacles!

79. Not so major innovations
with big monetary gains

80. Sports—NFL first down line
(1995-now)

81. Industrial Inspection: turbofan
jet engine blade maintenance
• Some seemingly daunting
machine vision tasks actually
works with relatively simple
image processing algorithms.

82. Industrial Inspection: Cognex Omniview

83. Industrial Inspection: Cognex Omniview

84. Object Recognition
Blue River Technology

85. Retail Insights
Source:
Prism Skylabs

86. Other Applications in
Business Intelligence
• Measure brand exposure.
• Measure sponsorship effectiveness.
• Loss prevention and retail layout optimization.

87. Some AI verticals that didn't
quite work out for me...
• Medical Imaging
• Multimedia
• Advanced Driver Assistance Systems (ADAS)
• Geospatial imaging for Energy Sector
• Surveillance
• Business Intelligence
• Advertisements

88. Intrinsic Imaging at Tandent
Vision Science
Computer Vision would be half-solved without shadows!
LightOriginal Image Surface

89. Tandent Lightbrush
Video Tutorial for Tandent Lightbrush: https://vimeo.com/47009123

91. Issues
• Highly anticipated, highly acclaimed, but small
crowd at $500 a license.
• Adobe Photoshop monopoly and the “not
invented here” syndrome.
• Adobe’s arch-rival, Corel (Corel Draw, Paint
Shop Pro, Ulead PhotoImpact) was DYING and
asked too much from the botched deal.

92. Have fun scribbling out your
shadows in photoshop!
Poor Bob from Adobe wasted 9 minutes removing just 1 shadow

93. Intrinsic Imaging for improving the
RGB signal in autonomous driving

94. Intrinsic Imaging’s other
applications

95. Retrospect
• 20 researchers burned 25 million in 8 years;
investors got 50 patents in return, period.
• Overestimated the total addressable market
size, in a market with existing monopoly.
• Many missed opportunities. Counterexample of
the lean startup model.

96. Some SfM, SLAM startups

97. Satellite/Aerial Imagery Analysis
• 40cm resolution at 30fps for 90 sec for any location on earth.
• One LEO satellite revisits any place on Earth every 3 days.
• Need 24 satellites to revisit any place on Earth every 3 hours.

98. Challenges for Single satellite depth
estimation and 3D reconstruction
• At 30fps, a LEO satellite
travels 250m between two
consecutive frames —>
theoretically sufficient for
cm-level depth estimation.
• Sources of Noise:
• Camera distortions
• Atmospheric Disturbance
• Ground vegetation
• Sub-pixel sampling noise
1
2

99. What happened?
• B2B customers takes too long to strike deals.
• Google ate us alive in just 3 months, while we
were still pitching for VC-funding with our
prototype.

100. Visual Search at Nervve

101. Retrospect
• Growth pains expanding from intelligence
community clients to advertisement clients.
• Forming the right team of engineers and
researchers and moving at the right pace.
• For any Computer Vision/Machine Learning
company:
• Researchers that cannot program—> OUT
• Engineers that don’t know math —> OUT

102. Viscovery = Video Discovery
Optical Character
Recognition
Offline
Recognition
2013
2014
Product Recognition
2015
Video Content related
Advertisements
2017
Wearable Devices
Video Content Discovery &
Interaction
2016
Leading provider of visual search technology in APAC

103. Once in a lifetime opportunity in
China’s video streaming market
Traditional
Media
Free Content,
Ad Revenue
Subscription
Revenue
Do Nothing?
Sitting Duck!
Improve Ad
Revenue
Content-
related video
ads
Own platform
Shared/
congregated
platform
customized
recommendatio
ns
ad measure /
brand safety
Image/Video
content owners
struggling to
monetize
Can AI solve it all?

104. Our initial focus:
Content-related advertisements

105. Why not go ahead and analyze all videos? COST!
0600120018002400
2014 2015 2016 2017 2018 2019
Revenue Hardware Cost Data Collection Cost Model Training Cost
Technology Inflection Point:
• Computer vision cost has dropped significantly
while market demand has increased beyond
exploratory stages.
• Overall cost reduction resulted from accumulated
domain expertise, efficient machines learning
techniques, and lower computing cost.
Case study on leading e-commerce site in APAC, with 1M SKU and >20M calls/month.

106. • Business model vs videos analyzed
Need to deliver value while analyzing!
o2o marketing
ad effectiveness
analysis
campaign
effectiveness
analytics
direct-sell
content-related ads
programmatic
content-related ads
Big Data Insights
0 2.5 5 7.5 10
videos
analyzed
(10^N) • Extremely costly to
analyze video content.
Google charges
$1.5/1000 images. with
3600 seconds/hour, it
will cost $5.4 / hour
video / single single
recognition task.
• Costly to store videos
and their recognition
results.

107. Hindsight on "right approach" for media
Increase user’s time spent & stickiness
Multiply traffic w/o subsidizing growth.
(4) Video content-related ads & e-commerce
(3) Ad exposure measure
(2) Content recommendation
Grow user & advertiser
budget organically with
new services
(1) Image/Video Content Recognition as a Service
Focus on
Revenue
Growth
(5)DataMiningandAnalyticsasaService
Goal
Media
Size

108. Value proposition to media:
Start making money the first day!
user/time/ad/budgetincrease(times)
0
1
2
3
4
TotalSiteRevenueIncrease(times)
04.5913.518
current Better recommendation
Improve user stickiness
Better measurement
Grow user & ad budget
Content-related ad, EC
Focus on Revenue
# users time / user ad / user*time budget / ad total site revenue

109. Viscovery's Complete Solution
for Media and Beyond
Media
AdRetail
Archive & Search
Content
Recommendation
Video Generator*
Brand Insight
Ad Effectiveness
Measure
Brand Safety
Assurance
Visual
Content
Recognition
Smart Checkout
Video to EC
Consumer Insights

110. Media AI: Video content archive & search

111. Media AI: Content Recommendation
Article-to-Article, Article-to-Video, Video-to-Video
The 31 Tiniest Cat Breeds
Cats are notoriously small
household pets, but some are
even tinier than the norm. If you
have tight living quarters or
simply can’t get enough of cats
little enough to resemble kittens
forever, there are plenty of small
cat breeds to suit you. When it
comes to cats, bigger isn’t
always better, as some of the
most charming felines come in
small packages.
Meaningful
Terms Re-weighting
High-dimension
Mapping
Terms (Attribute)
Extraction
Para

112. Media AI: Video Generator
powering the "pivot to video" movement!
Text Analysis
(NLP)
Image
Recognition
Key Phrases
Sentiment
Audio
Subtitles
F/O/S
Concept
Media
Database
Generation Engine

113. Ad AI: ad exposure measure
# Appearances 68 times
# Appearances with
Duration > 5 sec
2 times
# Appearances with
Duration > 15 sec
0
# Appearances with
Duration > 30 sec
0
Longest Duration of
Appearance
9 sec
Average Duration of
Appearance
2.12 sec
Average Appearance
Interval
37.79 sec
Total Duration of
Appearance
144 sec
Ad Exposure for Audi @ China GT (Shanghai) 2017
# times of logo
occupying >5%
screen size
8 times
Duration of logo
occupying > 5%
screen size
16 sec
# times of logo
occupying > 10%
screen size
0 times
Duration of logo
occupying > 10%
screen size
0 sec
Average logo size
(relative to screen)
2.12%
Maximum logo size
(relative to screen)
11.05%

114. Ad AI: Content-related ads

115. Ad AI: Brand safety assurance
Terroris Violence
DrugPolitical Explicit Content

116. Retail AI: o2o redirection

117. Retail AI: smart checkout
Oreo cookie ……………
$4.95
Bounty Paper Towel … $3.45
Cal. Red wine………… $12.44
--------------------------
Total
$20.84
Scan payment QR Code:
wechat Alipay

118. Smart checkout demo

119. Retail AI: merchandise management
•2D+3D cameras for head and eye tracking
•Detect Point-Of-Interest
•Consumer profiling and data collection
•Integrate with kiosk big screen devices
•ESL Integration
Smart POS
• Smart check out
• Client-facing POS screen
with 3D camera
• POS-on-the-run
• Customer Big Data
• ESL integration
Smart Shelf

120. Retail AI: Cunsumer Insights
Female, 30-35
Ms. Mary Rice
Status: VIP
Housewife
Brand: P&G,
LV, Nike,
Tiffany
Already
holding
product A
Reaching for
product B on
the shelf
E.g., People who appear interested in milk powder will
get an eDM containing relevant promo info.
Recognize customer appearance and behavior characteristics
to establish a buying recommendation database.

121. • Clear segment focus: Any corporation smaller
than Google, Facebook, Baidu, Alibaba,
Tencent.
• Need to go from 0-80 fast. Then, help them go
from 80-90 and catch up quickly.
• How can we make the "0-80" part scalable?
Building Technology Barrier

122. Sample-less Face Recognition

123. Merchandise Recognition

124. Some notes
• Managing innovation and delivering promises.
• Visionary leader v.s. stubborn fool v.s. big liar.

125. When something is important enough,
you do it even if the odds are not in your favor.
Elon Musk
Falcon 9
takeoff
Falcon 9
decelerate
Falcon 9
vertical
touchdown

126. Thank You!
albert@viscovery.com