Comments (2)
Mainly because the one-to-one matching strategy and the definition of HOI instance. For example, given an image, their is a person sitting on a horse, and the GT contains 3 HOI categories, e.g. (person, ride, horse), (person, sit_on, horse), (person, straddle, horse).
In training, the model directly outputs 100 HOI instances, the ground truth size is also 100 with 3 positive instances and 97 null padding. That is to say, there are only 3 slots responsible for the positive predictions.
In inference, if given this image, only 3 of the 100 slots are actived, so, after filterring, the model only outputs 3 HOI instances.
To summary, in the set prediction view, we don't think the original GT is one element with 3 labels, but 3 different elements.
The 'Appendix A: Illustration of Inference Process' part may help.
I hope it helped you, thanks.
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Thanks, it helps me a lot
from hoitransformer.
Related Issues (20)
- detr training
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