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I'm confused about what's so special here. This is analogous to a person from the early days of photography having no knowledge of the possibility of image doc

I'm confused about what's so special here. This is analogous to a person from the early days of photography having no knowledge of the possibility of image doctoring, and us feeling "smug" because a photo like this would elicit confusion. It seems to me you just have to run your NN on pairs of doctored and undoctored images, and create a separation of concern between scene continuity and object recognition. It's likely that a lot of implementations currently rely too much on surrounding context for successful categorization. It's also worth exploring certain image analysis techniques during pre-processing to draw out certain attenuated aspects. Edge detection is par for the course, but things like shading are also important to attenuate so that the NN has an easier time learning. If we're dealing with multiple frames and not just a single image, this opens up a whole dimension of temporal analysis that should make it trivial to separate the background from juxtaposed images.

PurpleBoxDragon 8y ago
>It seems to me you just have to run your NN on pairs of doctored and undoctored images, and create a separation of concern between scene continuity and object recognition. This may fix this problem, but what other visual issues will we uncover that might be fixable with the right kind of training but which we haven't yet accounted for. For example, we see so many optical illusions because of how our eyes and brain (and the neurons between them) work. These are often times caused by our sensation or perception incorrectly picking up data, but on purpose (well, as much purpose as an evolved system could have). For example, how neurons in the eye exaggerate differences in brightness or how we are overly sensitive to perceiving faces so that we see them even where they don't exist. What impact does the lack of these have on an artificial vision system? For starters, it would likely indicate the system wouldn't recognize the optical illusion unless trained for it, but are there any other impacts that we need to take into consideration, especially when considering having human lives depending upon the system?

PurpleBoxDragon 8y ago

>It seems to me you just have to run your NN on pairs of doctored and undoctored images, and create a separation of concern between scene continuity and object recognition. This may fix this problem, but what other visual issues will we uncover that might be fixable with the right kind of training but which we haven't yet accounted for. For example, we see so many optical illusions because of how our eyes and brain (and the neurons between them) work. These are often times caused by our sensation or perception incorrectly picking up data, but on purpose (well, as much purpose as an evolved system could have). For example, how neurons in the eye exaggerate differences in brightness or how we are overly sensitive to perceiving faces so that we see them even where they don't exist. What impact does the lack of these have on an artificial vision system? For starters, it would likely indicate the system wouldn't recognize the optical illusion unless trained for it, but are there any other impacts that we need to take into consideration, especially when considering having human lives depending upon the system?