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About loss function

See original GitHub issue

Hi, I found that the loss used in this repo is a cross-entropy loss between prediction and mask.

loss = F.binary_cross_entropy_with_logits(pred, mask)

But the loss mentioned in the paper is a contrastive loss between visual and textual features.

Issue Analytics

  • State:open
  • Created a year ago
  • Reactions:11
  • Comments:8 (1 by maintainers)

github_iconTop GitHub Comments

tiger990111commented, Oct 25, 2022

I have the same query. Can the authors please clarify?

Deepayan137commented, Jul 9, 2022

Hello Derrick,

I had seen this implementation. In your paper, you have mentioned equations 9 and 10 as the contrastive loss between pixel embeddings and the text features. I am not able to understand, how it is taken care of in your above code snippet?

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