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No, that appears to be incorrect. It is the case that with completely generative models, you will get hallucinated details very likely to be untruthful. But w
by montroser 2y ago
No, that appears to be incorrect.
It is the case that with completely generative models, you will get hallucinated details very likely to be untruthful. But with this approach, you can see blurry input images of license plates that with our naked eye we could not possibly decipher the characters, then put through this model where the output is very close to the actual ground truth.
https://dachunkai.github.io/evtexture.github.io/static/images/quanlitative_reds.png https://dachunkai.github.io/evtexture.github.io/static/image...
Where does this information come from? It seems they are generating synthetic "event camera-like" events just from diffs between still frames? So maybe they trained a model based on real events from a real event camera? It's hard to tell from their write-up. But these results are very impressive.
- spookie 2y agoYou don't need these kinds of solutions for license plates, take a look at this: https://www.youtube.com/watch?v=19wgu5GZDhk https://www.youtube.com/watch?v=19wgu5GZDhk
- the8472 2y agoNormal upscaling is picking pixel values in the higher-resolution target image that when run through an equivalent downscaling function would fit the smaller image. There are lots of degrees of freedom to make things up. Then there is undoing reversible transforms, such as some blurs. That makes information that was there all along more legible. Such as the example you have there. This paper is a case of both. It does upscaling, but it uses temporal information to find additional constraints that can be used to restrict the degrees of freedom of the "making values up" part. So it's part information recovery, part hallucination.
- vessenes 2y agoIn the GitHub comments they say what they are doing to infer events is significantly different than per frame diffs. I have no idea what they are actually doing though.