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From what I understood, the only difference between mask proposal network in this repo is the number of classes predicted (binary vs N dataset classes) and N predictions can be converted to binary predictions. Are there any other difference? Just curious, have you done any ablation to verify if binary prediction is necessarily better?
The text was updated successfully, but these errors were encountered:
The models from Mask2Former are mask-aware as they utilize a classification head, which would make the mask proposal network biased toward the base classes causing the performance of the novel classes to be lower. The class-agnostic mask proposal network, on the other hand, learns to output more general object/stuff masks without the awareness of each specific class information and thus would be more suitable for open-vocabulary image segmentation. Hope this helps!
From what I understood, the only difference between mask proposal network in this repo is the number of classes predicted (binary vs N dataset classes) and N predictions can be converted to binary predictions. Are there any other difference? Just curious, have you done any ablation to verify if binary prediction is necessarily better?
The text was updated successfully, but these errors were encountered: