Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Classification by Improving Labelling: Introducing
  Probabilistic Multi-Label Object Interaction Recognition   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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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin