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arXiv · 1703.08338

Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition

Michael Wray·Davide Moltisanti·Walterio Mayol-Cuevas·Dima Damen
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Abstract

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.

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Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas, Dima Damen. 2017-04-21. Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition. https://arxiv.org/abs/1703.08338

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