cross-scale problem

[chchshshhh]

Very interesting work, but why only the loss of two scales is crossed, and A1A2 is not added for cross-scale loss?

[TheoPis]

Thanks for the question. In general, there is no reason not to add more cross-scale terms (e.g A1-A2 A2-A3 ...). Adding terms would slightly increase training time but it is possible it could improve performance. In practice, we opted for cross-scale terms with features that have a slightly higher scale difference than A1-A2 (x2), namely A1-A3 (x4) and A1-A4 (x8). The motivation here was to better capture local vs global properties of the classes in the contrastive loss. We have not systematically tested all other possible cross-scale term options as we found that A1-A3 and A1-A4 worked well for many models/datasets.

[chchshshhh]

Thanks for the question. In general, there is no reason not to add more cross-scale terms (e.g A1-A2 A2-A3 ...). Adding terms would slightly increase training time but it is possible it could improve performance. In practice, we opted for cross-scale terms with features that have a slightly higher scale difference than A1-A2 (x2), namely A1-A3 (x4) and A1-A4 (x8). The motivation here was to better capture local vs global properties of the classes in the contrastive loss. We have not systematically tested all other possible cross-scale term options as we found that A1-A3 and A1-A4 worked well for many models/datasets.

Thank you very much for your answer. I have another question about the weight of loss, why does the setting of [1.0 0.7 0.4 0.1] work well? Is there any basis for that? or do I understand that in cross-scale learning, shallow contrast learning is more important and can lead to deeper semantic feature learning?