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About loss function
See original GitHub issueHi, 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.
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- Created a year ago
- Reactions:11
- Comments:8 (1 by maintainers)
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I have the same query. Can the authors please clarify?
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?