English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This work deviates from easy-to-define class boundaries for objectinteractions. For the task of object interaction recognition, often capturedusing an egocentric view, we show that semantic ambiguities in verbs andrecognising sub-interactions along with concurrent interactions result inlegitimate class overlaps (Figure 1). We thus aim to model the mapping betweenobservations and interaction classes, as well as class overlaps, towards aprobabilistic multi-label classifier that emulates human annotators. Given avideo segment containing an object interaction, we model the probability for averb, 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 twopublic datasets, comprising 1405 video sequences for which we provideannotations on 90 verbs. We outper- form conventional single-labelclassification by 11% and 6% on the two datasets respectively, and show thatlearning from annotation probabilities outperforms majority voting and enablesdiscovery of co-occurring labels.

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