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A Year in Computer Vision
- rememberlenny 9y agoThis is the most thorough write-up around computer vision. Can anyone else recommend other equally broad works worth looking at?
- krat0sprakhar 9y ago+100! This is absolutely amazing! Would love to see a similar high-level overview of progress in other tasks where CNNs (or other neural nets) have had such a tremendous impact.
- bufferoverflow 9y agoTwo Minute Papers on YouTube does short overviews of cutting edge graphics and vision research. https://youtube.com/user/keeroyz https://youtube.com/user/keeroyz
- daguava 9y agoAt 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 9y agoYeah, I saw that. It seems that the algorithm was trained and optimised only for that video. Person, bike, suitcase, tie, chair, cellphone.
- yters 9y agoCar AI scenario: classifying human as pavement with 100% certainty.
- jacquesm 9y agoThat's better than having the algorithm detect your camera as a weapon.
- m3kw9 9y agoIs 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.
- ktta 9y agoI believe it is because much of the ideas were already formulated, but the recent advancements in silicon tech, especially GPUs was a huge contributor. Pair that will exceeding amounts of data being collected from/posted by users, you're got a great catalyst for major companies to be interested, which in turn lead to more research and refinement of old ideas.
- deepnotderp 9y agoYup, most of the progress, such as YOLO, which, while impressive, ultimately essentially amounts to incremental innovation, there really haven't been any paradigm upturning changes.
- flukus 9y agoI thought it was slower. A lot of the core stuff goes back to the 60's and it's all been slow, incremental improvements since then. The biggest game changer has been hardware finally becoming powerful enough.
- Swizec 9y agoRemember 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 9y ago> Transistor from 1920s Since 1947, when it was invented.
- nightsd01 9y agoWhat’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”
- blauditore 9y agoI've heard that claim a couple of times now, but never read or heard experts in the field say anything like that. Do you have a source? Of course, you can do fancy things with neural networks without fully understanding how they work, or why they behave like they do. But my impression is that common applications, like image classification, are quite well-understood now.
- valesco 9y agoI think you are right to make that distinction. Research papers are probably more about other, less known applications.
- deleted 9y ago[deleted]
- hacker_9 9y agoWhat? They do explain their results, and as you can see from the references section of any paper, other researchers read the published research and then build their new models using that information and continually refining the process.
- amelius 9y agoYes, they explain it, but in a very hand-wavey way.
- reiinakano 9y agoSure, they do their best to give an explanation after discovering it works, but nobody ever publishes the hundred other models they tried that don't work out.
- deepsun 9y agoNothing about capsules?
- Smerity 9y agoCapsule 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
- alexbeloi 9y agoIt's new and interesting, but hasn't outperformed any existing methods yet. High representation efficiency though.
- eref 9y ago> High representation efficiency Capsules do require much fewer parameters, they generalize 10-20% better to new viewpoints, they are much more robust to adversarial examples and can better recognize overlapping objects; but, on the other hand, capsules currently require much more training data to achieve the same performance, even though in theory (if they would actually learn inverse graphics) they should require less data, and they add a lot of expensive additional structure (roughly 10X). I am rather pessimistic about whether the approach will lead us anywhere; it seems sub-optimal to model all possible child-parent configurations explicitly. That has a quadratic nature to it and my hunch is that can be done sub-linearily.
- deleted 9y ago
- jerrre 9y agoCould 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).
- vlehto 9y agoIt 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 9y ago"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.
- chrisan 9y ago> 6.) FaceID for the iphoneX. I'm personally loving it on mine. Sorry kinda OT from malovent/benevolent route, but is FaceID fast? Or rather, is it just as fast as the normal way of unlocking with finger? Do you have to tilt your phone to your face or is there some kinda wide angle lens? I frequently unlock my phone laying flat on my desk
- vlehto 9y ago
- alexcnwy 9y agoExcellent! I'd suggest anyone interested in jumping into building computer vision models should check out the excellent free MOOC on fast.ai
- SirHound 9y agoThe 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
- nl 9y agoThis 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
- deleted 9y ago[deleted]
- protomok 9y agoGreat 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.
- nbzs 9y agoThere is no ethical consideration in computer science. There are goals on a horizon, souls to be stolen, identities to be classified, big money to be made. If someone is still asking why there is ageism in tech, the answer is not in performance of the elders. The answer is that when you are young, you are full of shit and its easy to be trained like pavlov dog. It is a natural thing, young people are more likely to be naive and easy to mislead. On other hand how you like your iPhone X? It is not secure, yet. After a year of training it will be. Face it people, privacy is dead.