One-hot or multi-hot-encoding? about na-fwebsod HOT 2 CLOSED

In flickr-clean, each image contains only 1 category. Is this what you expect or just a special case?
If the training data just contain a single object, will it lack enough feature to train a powerful object detector?

from na-fwebsod.

Hi, I find that in your dataset flickr-clean, each image contains only 1 category. In flickr-voc, some images contain 2 categories or more. In the latter case, should the label be multi-hot? For example, an image has two annotations [category_id:0] and [category_id:2]. Should the label be [1,0,1,……,0]?

Yes, you are right. The paper uses one-hot labels to expose our method and it is easy to generalize to multi-hot labels.

In flickr-clean, each image contains only 1 category. Is this what you expect or just a special case?

flickr-clean is not created by us.
I think it is expected.

If the training data just contain a single object, will it lack enough feature to train a powerful object detector?

I agree with this.
I think it will lack the images with multi-class co-occurrence.

from na-fwebsod.