Ruiz A, Martinez O, Binefa X, Sukno FM. Fusion of Valence and Arousal Annotations through Dynamic Subjective Ordinal Modelling. In Proc. 12th IEEE Conference on Automatic Face and Gesture Recognition, Washington DC, USA, in press, 2017
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Ruiz A, Martinez O, Binefa X, Sukno FM. Fusion of Valence and Arousal Annotations through Dynamic Subjective Ordinal Modelling. In Proc. 12th IEEE Conference on Automatic Face and Gesture Recognition, Washington DC, USA, in press, 2017
A. Ruiz, O. Martinez, X. Binefa and F.M. Sukno. Fusion of Valence and Arousal Annotations through Dynamic Subjective Ordinal Modelling. In Proc. 12th IEEE Conference on Automatic Face and Gesture Recognition, Washington DC, USA, in press, 2017.
An essential issue when training and validating computer vision systems for affect analysis is how to obtain reliable ground-truth labels from a pool of subjective annotations. In this paper, we address this problem when labels are given in an ordinal scale and annotated items are structured as temporal sequences. This problem is of special importance in affective computing, where collected data is typically formed by videos of human interactions annotated according to the Valence and Arousal (V-A) dimensions. Moreover, recent works have shown that inter-observer agreement of V-A annotations can be considerably improved if these are given in a discrete ordinal scale. In this context, we propose a novel framework which explicitly introduces ordinal constraints to model the subjective perception of annotators. We also incorporate dynamic information to take into account temporal correlations between ground-truth labels. In our experiments over synthetic and real data with V-A annotations, we show that the proposed method outperforms alternative approaches which do not take into account either the ordinal structure of labels or their temporal correlation.
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