You could create a two-stage classifier, where both pre-trained models’ features are fed into a second model, which tries to predict the actual classes.
You could train it end-to-end, which would be the simple way. If you would like to fine-tune both pre-trained models separately and then push the features to the second stage model, you would have to make sure to carefully create separate validation sets, as data leakage will be a problem, if you just stick to the vanilla train/val/test splitting.