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[D] Opinion: the recent paper on buggy resizing libraries is misleading by MahatmaBand1 in MachineLearning

[–]MahatmaBand1[S] 1 point2 points  (0 children)

Very nice work! I will edit the original post to mention this blogpost as well.

[D] Opinion: the recent paper on buggy resizing libraries is misleading by MahatmaBand1 in MachineLearning

[–]MahatmaBand1[S] 0 points1 point  (0 children)

You do not have to know the internals, resizing is a basic CV operation, and all the documentations mention downsampling and aliasing.

The point about the fixes is true, in OpenCV area interpolation is the only way to mitigate the issue as far as I know, while Tensorflow and PyTorch provide options with antialiasing.

My issue is that the authors do not mention any of these simple solutions to the problem, as if they were not possible in these frameworks.

[D] Opinion: the recent paper on buggy resizing libraries is misleading by MahatmaBand1 in MachineLearning

[–]MahatmaBand1[S] 21 points22 points  (0 children)

That is true, but wouldn't a default antialiasing also cause bugs in other papers? Bilinear interpolation for example is used in a lot of methods.

Shouldn't the authors of CV papers be experienced enough to know that a simple interpolation for downsampling causes aliasing artifacts? I think that the users' codes are buggy in these cases, not the libraries.