  Interactive Machine Teaching systems allow users to create customized machine
learning models through an iterative process of user-guided training and model
assessment. They primarily offer confidence scores of each label or class as
feedback for assessment by users. However, we observe that such feedback does
not necessarily suffice for users to confirm the behavior of the model. In
particular, confidence scores do not always offer the full understanding of
what features in the data are used for learning, potentially leading to the
creation of an incorrectly-trained model. In this demonstration paper, we
present a vision-based interactive machine teaching interface with real-time
saliency map visualization in the assessment phase. This visualization can
offer feedback on which regions of each image frame the current model utilizes
for classification, thus better guiding users to correct the corresponding
concepts in the iterative teaching.
