- Accepted at UAI 2022 Causal Representation Learning Workshop
- Causal CAM (C^2AM), is a simple yet effective algorithm for enhancing CAM quality. It requires no extra parameters, modification of the classification network architecture, or manipulating of the images. Further, the implementation of C^2AM is also straightforward, it only requires one additional line of code.
# Classification forward pass
x = cls_model(imgs) # x.shape == B * 20
# Multiply with Global CAM
# x = x.unsqueeze(2).unsqueeze(2) * global_cam # x.shape == B * 20 * H * W
# Mean Pooling
# x = torch.mean(x, dim=(2, 3)) # x.shape == B * 20
# Ours
x = torch.mean(x.unsqueeze(2).unsqueeze(2) * global_cam, dim=(2, 3))
# Loss
loss = torch.nn.BCELoss()(x, labels)
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We evaluated C^2AM on PASCAL VOC 2012 and achieved mIOU 69.6% of the pseudo mask generation on the training set, and mIOU 67.5% and 67.7% on validation and test set when training DeepLabV2 on the seed masks.
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Weights are released at here.
