Title: Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,
Abstract: Acquisition of labeled training samples for affective computing is usually
costly and time-consuming, as affects are intrinsically subjective, subtle and
uncertain, and hence multiple human assessors are needed to evaluate each
affective sample. Particularly, for affect estimation in the 3D space of
valence, arousal and dominance, each assessor has to perform the evaluations in
three dimensions, which makes the labeling problem even more challenging. Many
sophisticated machine learning approaches have been proposed to reduce the data
labeling requirement in various other domains, but so far few have considered
affective computing. This paper proposes two multi-task active learning for
regression approaches, which select the most beneficial samples to label, by
considering the three affect primitives simultaneously. Experimental results on
the VAM corpus demonstrated that our optimal sample selection approaches can
result in better estimation performance than random selection and several
traditional single-task active learning approaches. Thus, they can help
alleviate the data labeling problem in affective computing, i.e., better
estimation performance can be obtained from fewer labeling queries.