Comments (2)
Hi @wangbo-zhao, thanks for your interest in our work. Indeed, this conflict could happen. The chance of conflict is the same as is during any vision-language model like CLIP's training (which samples images belonging to a set of classes).
To re-iterate, the main motive of contrastive loss is to establish the differences between different tasks, as the number of binary masks varies depending on the task for the same image.
Nonetheless, we uniformly sample the task to decrease the chance of such conflicts during our joint training process. Moreover, due to the random sampling of images, it is unlikely that it would happen for many batches. Still, would be interesting to quantify the chance of such conflicts (which would depend on the dataset).
from oneformer.
Thanks for your explanation.
from oneformer.
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from oneformer.