  This work deviates from easy-to-define class boundaries for object
interactions. For the task of object interaction recognition, often captured
using an egocentric view, we show that semantic ambiguities in verbs and
recognising sub-interactions along with concurrent interactions result in
legitimate class overlaps (Figure 1). We thus aim to model the mapping between
observations and interaction classes, as well as class overlaps, towards a
probabilistic multi-label classifier that emulates human annotators. Given a
video segment containing an object interaction, we model the probability for a
verb, out of a list of possible verbs, to be used to annotate that interaction.
The proba- bility is learnt from crowdsourced annotations, and is tested on two
public datasets, comprising 1405 video sequences for which we provide
annotations on 90 verbs. We outper- form conventional single-label
classification by 11% and 6% on the two datasets respectively, and show that
learning from annotation probabilities outperforms majority voting and enables
discovery of co-occurring labels.
