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

TL;DR

This paper introduces a probabilistic multi-label classification approach for object interaction recognition, capturing semantic ambiguities and class overlaps to improve accuracy over traditional single-label methods.

Contribution

It proposes a probabilistic multi-label classifier that models annotation uncertainties and overlaps, outperforming conventional methods in egocentric object interaction recognition.

Findings

Outperforms single-label classification by 11% and 6% on two datasets.

Learns from annotation probabilities, surpassing majority voting.

Enables discovery of co-occurring interaction labels.

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.