Early Classifying Multimodal Sequences

Abstract

Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gained attention as a means of adapting classification to more dynamic environments. However, so far results have been limited to unimodal sequences. In this pilot study, we expand into early classifying multimodal sequences by combining existing methods. Spatial-temporal transformers trained in the supervised framework of Classifier-Induced Stopping outperform exploration-based methods. We show our new method yields experimental AUC advantages of up to 8.7%.