Care to elaborate?
Still vastly underused in image to image to this day and I don't understand why: Unets and self-attention. I keep seeing papers come out that have obvious problems that could be solved using these two simple things!
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Yea please expand!
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Hey
@citnaj, I recently had a go at implementing self-attention for images. I'm not sure if my implementation is correct. Would you mind taking a look? Github repo => https://github.com/theairbend3r/how-to-train-your-neural-net/blob/master/pytorch/basics/attention_in_computervision.ipynb … -
Sure will take a look tomorrow- quite late here now and I'm being super bad. But did it work for you?
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Maybe becaue image to image attention is much harder than with NLP. Journal of negative results is a view of mind, but trust me many researchers tried - in our case it was for virtual try. Yet, Conditional Batch Norm works well to modulate an img wrt a text.
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Can you expand on it being harder? It seems like a pretty easy thing to drop in a network...
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@citnaj is there a good example of a paper that uses both? I'm curious -
Not a paper but DeOldify of course :) . I'm not sure what to recommend to you otherwise but there's some work done in biomedical image segmentation that I've seen.
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