geoffreychen777/vc

Virtual Category Learning (ECCV 2022 oral and T-PAMI)

vc's Issues

About the classification loss calculation

Hi, thanks for your such great work, but I have a little confused about the calculation of classification loss in your code: from my understanding, the logits sent into the focal loss have the shape of [bached_roi_bbox_num, num_classes + 1 + batched_roi_bbox_num], in the last dimension, from 0 to num_class, it is in the original classification label, num_classes means background class, and from the num_class + 1 to num_class + batched_roi_bbox_num, it represents the similarity scores of the batched roi boxes from a different view， if the label is -1 means ignore, so, could you tell me which activation function you used in the focal loss? softmax or sigmoid? for there may be multiple "1" in the last dimension? How did you deal with this situation? and what's the meaning of the calculation CE in the loss function in your code? why do we need plus 1 to the "pos_term * neg_term"?
Hope to get your more detailed explanations for this. Thanks!