K-Fold Cross Validation for NNs When using K-Fold CV, is it still useful to have a Train/Validation/Test split?

Or simply just a Train/Test? I.e. split up data into k bins, and leave one out for testing, train on the rest, and take average of the scores.
It depends.  If you're evaluating your model's performance without tuning hyperparameters, then a train/test split is sufficient.

If you're tuning hyperparameters, then you need a validation set.  Within each fold, you'll train on the training set (of course), using the validation set to tune hyperparameters.  Then you'll evaluate performance on the test set.