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It may not have a sophisticated enough vocabulary to distinguish 'pink' when 'red' was close enough. This effect is manifest in human languages which classify c
by Xophmeister 12y ago
It may not have a sophisticated enough vocabulary to distinguish 'pink' when 'red' was close enough. This effect is manifest in human languages which classify colours differently: say, for example, a language may have no word for 'blue', so the sky is 'green' to its speakers; it's still perceptually different to them, of course, but the lack of fidelity means it can't be communicated better than "sky green" or "grass green".
- Trufa 12y agoI understand there are a lot of subtleties, but it's still surprising for me that it can recognize a parked motorcycle from an awkward angle but can't distinguish normal english pink from red. Just to be clear, I don't mean it as a criticism, it just seems to be the easier part.
- ajuc 12y agoThere was no pink motorcycle in the training dataset most probably, and neural net may have failed to generalize colors beetween the objects. Similar error - yellow passanger car is described as yellow school bus - school bus is more common in yellow color.
- DanBC 12y agoAbove the image of the pink (but labled red) motorbike there is an image of a child wearig a wooly hat. The hat is red with white fluff. That hat is labled pink. So, it knows "pink". And the motorbike isn't borderline pink / red -- it's not like hunting pinks -- it is definitely pink. Having said all that I'm amazed at the results. It feels like I'm living in the future.