> The latest version of MemNet is available online. Being an amateur cat photographer myself, I decided to give this a try. Apparently, the most memorable part of Mr. Tango Tangerine’s face is his left ear
The cat photo is pretty ordinary, as far as photos of cats taken by their loving owners go...though I could imagine why the algorithm behaved the way it did, id be interested in hearing anyone try to argue that the algorithm picked something remotely relevant to the human experience. I mean, if the ear were deformed or on fire, sure...but it's not interesting in any way, even if you take the tack of "we'll all cats look the same anyway so no one will remember the cats face"
That said, a huge kudos to the MIT researchers for not only open sourcing their work, but releasing a straightforward REST API to make it easy for anyone to test out their algorithm.
The applications for this in the advertising industry are exciting and a tad bit scary.
I can imagine a feedback system that takes the output of this algorithm and modifies the image slightly according to the error gradient to optimize the MEM-score of the brand/item being advertised in the photo. Then it could feed the new image back in and repeat like the Deep-Dreaming algorithm.
side-note: I'm so happy that CSAIL is finally embracing deep learning. I'm an undergrad at mit and this semester was the first that deep learning was a major part of both the computer vision class and the nlp class.
Although your idea is absolutely terrifying, I could see it having some pretty entertaining failure modes.
For example: a sportswear company uses an image destined for their Instagram feed as input. It's a photo of a famous athlete touting their product. After they process the image, the output has a higher MEM-score—but only because the athlete is now dressed in the attire of their competitors.
In another thread, someone mentioned a mobile app that paused ad videos if you weren't looking at the device. Maybe the approach you mention could be applied in a similar way - to morph images in ads until you do look at them.
This problem has been mostly solved. You don't need a deep learning algorithm to detect a face. There's an entire company that has sprouted from online education by providing solutions for the proctoring of exams (the webcam is turned on, and facial movement that suspiciously turns towards offscreen areas is flagged). I'm not even sure the phone's camera would have to be on to do the mobile app detection you describe.
That's what I meant - the application that detect whether you are looking at an ad is already out there. What I meant was that rather than simply pausing the video it could change the image iteratively until you do look at the image.
I don't see this being of huge use to most media campaigns in which a human editor is involved...though perhaps it could be one of several first-pass filters used to go through a digital photographer's memory card to and filter out the weakest images (personally, as a photographer, a much simpler tool that can weed out obviously blurry or unfocused images would be much, much, much more useful than something that tells me what I hope I already know and enjoy doing: picking out my favorite photos)
However, this algorithm would be immediately useful for people who need to auto crop photos in a way more intelligently than "just fit this dimension and ratio"...but this function has been implemented to some degree by various other computer vision systems, such as Microsoft's Projext Oxford https://www.projectoxford.ai/vision
Maybe all the world is uploading pictures right now but after several minutes the site is still "Computing..." the one I uploaded. It's only me? I tried with Firefox and Opera.
> While deep-learning has propelled much progress in object recognition and scene understanding, predicting human memory has often been viewed as a higher-level cognitive process that computer scientists will never be able to tackle
> For each image, the algorithm produces a heat map showing which parts of the image are most memorable. By emphasizing different regions, they can potentially increase the image’s memorability.
Most memorable, according to human subjects subjective thought on the matter?
> The team then pitted its algorithm against human subjects by having the model predicting how memorable a group of people would find a new never-before-seen image. It performed 30 percent better than existing algorithms and was within a few percentage points of the average human performance.
Who's to say human subjects are any good at objectively judging how memorable a photo is? I feel like I'm missing something.
Edit: Riight, I guess it could be based on observing neural activity in human subjects while they look at photos. That makes a lot more sense.
Sounds like maybe this could be used for deep-learning networks to learn what to pay attention to. The memorable regions should be the salient or important regions in the image. Basically the more you are paying attention, the more memorable something is.
Uploading a URL of white noise from Wikipedia gave it a high-memorability (0.82) with lots of areas of interest. Secondly uploading a purely white image [2] produced high areas of interest in the top corners and a mediumly interesting image (0.62).
I thought the tests might reveal something useful, like the eye-tracking heat-maps of Jakob Nielsen [3] but I'm not convinced.
A machine-learned system is only as good as its training data, at best. In this case, it was trained on natural-looking images, so results on "unnatural" images will be unpredictable/random/wrong.
One way to fix this would be to provide "bug bounty"-style rewards for producing images that makes the system deviate significantly from mechanical turk workers performing the same task. I wouldn't be surprised to see google/fb etc starting such programs in the near future, as their ML systems reach maturity.
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[ 2.8 ms ] story [ 64.8 ms ] thread> The latest version of MemNet is available online. Being an amateur cat photographer myself, I decided to give this a try. Apparently, the most memorable part of Mr. Tango Tangerine’s face is his left ear
The cat photo is pretty ordinary, as far as photos of cats taken by their loving owners go...though I could imagine why the algorithm behaved the way it did, id be interested in hearing anyone try to argue that the algorithm picked something remotely relevant to the human experience. I mean, if the ear were deformed or on fire, sure...but it's not interesting in any way, even if you take the tack of "we'll all cats look the same anyway so no one will remember the cats face"
That said, a huge kudos to the MIT researchers for not only open sourcing their work, but releasing a straightforward REST API to make it easy for anyone to test out their algorithm.
I can imagine a feedback system that takes the output of this algorithm and modifies the image slightly according to the error gradient to optimize the MEM-score of the brand/item being advertised in the photo. Then it could feed the new image back in and repeat like the Deep-Dreaming algorithm.
side-note: I'm so happy that CSAIL is finally embracing deep learning. I'm an undergrad at mit and this semester was the first that deep learning was a major part of both the computer vision class and the nlp class.
For example: a sportswear company uses an image destined for their Instagram feed as input. It's a photo of a famous athlete touting their product. After they process the image, the output has a higher MEM-score—but only because the athlete is now dressed in the attire of their competitors.
However, this algorithm would be immediately useful for people who need to auto crop photos in a way more intelligently than "just fit this dimension and ratio"...but this function has been implemented to some degree by various other computer vision systems, such as Microsoft's Projext Oxford https://www.projectoxford.ai/vision
https://people.csail.mit.edu/khosla/papers/iccv2015_khosla.p...
Seriously?
Has it seriously often been viewed like that?
The Reverse AI Effect: if it's thinking, then surely it can't be done by a computer.
Because Humans Are Special.
Most memorable, according to human subjects subjective thought on the matter?
> The team then pitted its algorithm against human subjects by having the model predicting how memorable a group of people would find a new never-before-seen image. It performed 30 percent better than existing algorithms and was within a few percentage points of the average human performance.
Who's to say human subjects are any good at objectively judging how memorable a photo is? I feel like I'm missing something.
Edit: Riight, I guess it could be based on observing neural activity in human subjects while they look at photos. That makes a lot more sense.
> The images had each received a “memorability score” based on the ability of human subjects to remember them in online experiments.
I thought the tests might reveal something useful, like the eye-tracking heat-maps of Jakob Nielsen [3] but I'm not convinced.
[1] https://upload.wikimedia.org/wikipedia/commons/f/f6/White-no...
[2] http://images.all-free-download.com/images/graphiclarge/plai...
[3] https://www.nngroup.com/books/eyetracking-web-usability/
One way to fix this would be to provide "bug bounty"-style rewards for producing images that makes the system deviate significantly from mechanical turk workers performing the same task. I wouldn't be surprised to see google/fb etc starting such programs in the near future, as their ML systems reach maturity.