More questions about ROI heads and pseudo-label generation

[vadimkantorov]

1. In what part of code do you handle this? In practice, conflicts happen when we force the yˆ(·, r) to be a one-hot vector since the same region can be chosen to be positive for different ground-truth classes, especially in the early stages of training. Our solution is to use that class for pseudo-label rˆ which has a higher predicted score s(c, rˆ).

2. What scores are used for generating supervision for student branches? It seems to me that you normalize scores across classes. Is it true? https://github.com/NVlabs/wetectron/blob/44e6fa95aee07d6722a62af56f016a3ae99bd8a6/wetectron/modeling/roi_heads/weak_head/loss.py#L257

Thanks!