
A Year in Computer Vision - Geeshang
http://www.themtank.org/a-year-in-computer-vision
======
vlehto
It amazes me that there is seemingly complete void of ethical consideration
here.

I can come up with handful of completely malevolent uses for facial
recognition in no time flat. Automated weapons, increased surveillance,
targeted marketing.

The only good use I can come up with is spotting terrorist. But they are
already the one group of people who seem to have the luxury of occasionally
using masks.

Involvement in this stuff could very well yield more problems with your
personal conscience than what Mikhail Kalashnikov has ever had. "Someone will
do it anyhow" Yes. But you can drive up the price and availability of
malevolence by personally avoiding it.

~~~
jjcm
"The only good use I can come up with is spotting terrorist"

I can come up with a handful of benevolent uses for facial recognition in no
time flat.

1.) Automated ID systems for supermarkets. Walk into a store, store knows who
you are. No checkout needed. Just walk out.

2.) Security for venues/work/etc. No more backstage passes getting copied -
computer vision knows who's allowed where.

3.) Emergency response after a disaster. Automatically notify loved ones if
you've been detected alive in a news feed.

4.) Voting systems. Got a stadium full of people? Just have them raise their
hands - computer vision does a tally along with ID'ing who voted for what.

5.) Guest lists / ticketing. Show up to a venue, walk in. No ticket needed.
Venue knows who's if you're allowed in. No more lost tickets.

6.) FaceID for the iphoneX. I'm personally loving it on mine.

Like many new technologies, it can be used for both good and bad. Harnessing
nuclear reactions can be used to generate massive amounts of electricity, or
to level cities. What's important is that it's regulated, not that it's
banned.

~~~
Fiahil
1.) Automated ID systems for supermarkets. Walk into a store, store knows who
you are. No checkout needed. Just walk out.

Of course, nobody will ever use this feature as a mean of enhancing their
"targeted marketing" nor creating a "targeted pricing" strategy. For me, this
is a big NO-NO and should go straight to the malevolent usage list.

~~~
emiliobumachar
If enough people won't tolerate price discrimination, there's money to be made
if a store will pledge not to do it.

But if there's only one walk-out supermarket available, and it does price
discriminate, and it's not too outrageous, I'm in. Waiting in line for
checkout is very bothersome for me, and I like my job very much, so I'll pay
more than my hourly rate at work to get out of each hour of line.

(extra bothersome if there's untargeted marketing blaring through some
speakers while I wait, 1984-style. But I already avoid those supermarkets)

Nobody likes price discrimination, but there are advantages. Poor people pay
much, much less. It's ironic that we're always coming up with inefficient
measures full of collateral damage to ease thing up on the poor, but we'll
rail against price discrimination like it's Salem.

~~~
fjsolwmv
Did you know grocery delivery is available?

~~~
emiliobumachar
Not in my city, but thanks for the intention.

------
nightsd01
What’s really fascinating about the current state of the art in computer
vision/deep learning is just how many of the top research papers basically say
“We tried this, it works, we’re not really sure why”

~~~
cbcoutinho
This has been my experience as well. I'm also am afraid that these researchers
will succumb to a similar replication crisis that devastated the social
sciences [0]. If you can't necessarily explain why something works, it's
probably difficult to explain trying it again in the future didn't work

[0]
[https://en.m.wikipedia.org/wiki/Replication_crisis](https://en.m.wikipedia.org/wiki/Replication_crisis)

~~~
_pastel
It's more like the opposite problem.

Psychology researchers try to justify deep, compelling theories of human
behavior with small experiments that are hard to reproduce.

Deep Learning experiments are comparatively easy to run and reproduce because
the field has both a common set of performance benchmarks and a large
community of people implementing cutting-edge ideas in common frameworks. The
harder part is building useful theory on top of those experiments.

------
rememberlenny
This is the most thorough write-up around computer vision.

Can anyone else recommend other equally broad works worth looking at?

~~~
bufferoverflow
Two Minute Papers on YouTube does short overviews of cutting edge graphics and
vision research.

[https://youtube.com/user/keeroyz](https://youtube.com/user/keeroyz)

------
m3kw9
Is it me or is the advance in AI seem much faster than any other tech that
came before it? The rapid advancement in just one year is pretty mind
boggling. Very hard to catch up.

~~~
Swizec
Remember Smartphones only started really existing in 2007. Mobile phones are
from 1990s. Personal computers from 1970s. Transistor from 1920s

Pace of innovation is generally increasing as more and more complex stuff
becomes commoditized. But also we forget just how recent a lot of things we
take for granted really are.

Give it 10 years and AI will feel just as common and boring as a laptop does
today. Remember how excited you were in the 1990s when you upgraded a new PC.

~~~
jacquesm
> Transistor from 1920s

Since 1947, when it was invented.

~~~
Swizec
Touche.

I must have been thinking of those glass things that came before transistors
and filled a similar purpose then

~~~
jacquesm
Radio tubes, specifically the Triode (basic amplification tube, a lot like a
transistor only not solid state and _much_ higher voltage). Invented in 1904.

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daguava
At 1,50 in the Yolo V2 youtube video I couldn't help but laugh when the
algorithm detected the gun in the woman's hand as a cell-phone, made me think
of the whole ET Walkie-Talkie/gun censorship scenario.

~~~
liviu
Yeah, I saw that. It seems that the algorithm was trained and optimised only
for that video. Person, bike, suitcase, tie, chair, cellphone.

------
SirHound
The standard of super-resolution algos is impressive. I wonder if anyone has
tried up-ressing the sound and audio of pirate videos by training models on
Cam vs Blu-ray

------
jerrre
Could be a fun game to guess the movie based on only YOLOv2s output
([https://www.youtube.com/watch?v=VOC3huqHrss](https://www.youtube.com/watch?v=VOC3huqHrss)).

------
protomok
Great summary of recent Computer Vision. Over 203 references!

One exciting topic I did not see mentioned (maybe I missed it) is image
denoising. Excellent paper here -
[https://arxiv.org/abs/1608.03981](https://arxiv.org/abs/1608.03981). The CNN
proposed in the paper is really compute intensive but the denoised images are
really amazing.

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nl
This is pretty nice work, but isn't it a summary of 2016 work?

ImageNet 2017 has happen since then[1] (small incremental progress), but lots
of progress in things like super resolution.

[1] [http://image-net.org/challenges/LSVRC/2017/results](http://image-
net.org/challenges/LSVRC/2017/results)

------
alexcnwy
Excellent!

I'd suggest anyone interested in jumping into building computer vision models
should check out the excellent free MOOC on fast.ai

------
deepsun
Nothing about capsules?

~~~
Smerity
Capsule networks[1] have literally not been used on anything larger than what
is considered a toy dataset for computer vision yet. Whilst there is potential
there I'd argue it's too early to ascribe extreme levels of excitement towards
it.

[1]: [https://arxiv.org/abs/1710.09829](https://arxiv.org/abs/1710.09829)

~~~
yogrish
Very good explanation of capsule networks:
[https://www.youtube.com/watch?v=pPN8d0E3900](https://www.youtube.com/watch?v=pPN8d0E3900)

