Paper claims perfect accuracy on image recognition datasets (arxiv.org)

At face value I'm suspicious, I thought that there were genuine ambiguities or label errors in some of those datasets that makes it very surprising you could even really define 100% accuracy.

Also, reading the paper a bit, it's either badly written and I just don't understand what they're saying at all, or BS. It doesn't really explain anything about their implementation, it just says they did do and got 100% accuracy, and throws in a bunch of jargon. Maybe I'm just not familiar with this area enough, but the way it's laid out raises even more red flags

ok but AFHQ dataset, Four Shapes, MNIST and CIFAR10 are baby datasets; do this on COCO or pascal VOC or imagenet...

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Also, can anybody share a more informal high-level intuition about what this ‘Learning with signatures’ approach is about? It seems to be a rather recent topic in Learning (paper cites 2019+ publications)