Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
This work addresses the challenge of ambiguous labeling in object interaction recognition for computer vision applications, representing an incremental advance in multi-label classification methods.
The paper tackles the problem of semantic ambiguities and class overlaps in object interaction recognition by introducing a probabilistic multi-label classifier that models annotation probabilities from crowdsourced data, achieving improvements of 11% and 6% over single-label classification on two datasets.
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.