Multi-instance dynamic ordinal random fields for weakly supervised dacial behavior analysis
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- dc.contributor.author Ruiz, Adrià
- dc.contributor.author Rudovic, Ognjen
- dc.contributor.author Binefa i Valls, Xavier
- dc.contributor.author Pantic, Maja
- dc.date.accessioned 2021-06-29T08:06:23Z
- dc.date.available 2021-06-29T08:06:23Z
- dc.date.issued 2018
- dc.description.abstract We propose a multi-instance-learning (MIL) approach for weakly supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only labels at bag-level are provided. Specifically, we consider the multi-instance dynamic-ordinal-regression (MI-DOR) setting, where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences. To this end, we propose MI dynamic ordinal random fields (MI-DORF). In this paper, we treat instance-labels as temporally dependent latent variables in an undirected graphical model. Different MIL assumptions are modelled via newly introduced high-order potentials relating bag and instance-labels within the energy function of the model. We also extend our framework to address the partially observed MI-DOR problem, where a subset of instance labels is also available during training. We show on the tasks of weakly supervised facial action unit and pain intensity estimation, that the proposed framework outperforms alternative learning approaches. Furthermore, we show that MI-DORF can be employed to reduce the data annotation efforts in this context by large-scale.
- dc.description.sponsorship This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 645012. The work of O. Rudovic was supported by the H2020 Research Program through the Marie Skodowska-Curie Grant Agreement under Grant 701236 (EngageME). The work of X. Binefa was supported in part by Spanish Government under Grant MINECO TIN2017-90124-P and in part by the Generalitat de Catalunya under Grant MINECO TIN2017-90124-P and Grant AGAUR 2017-SGR-1311
- dc.format.mimetype application/pdf
- dc.identifier.citation Ruiz A, Rudovic O, Binefa X, Pantic M. Multi-instance dynamic ordinal random fields for weakly supervised dacial behavior analysis. IEEE Trans Image Process. 2018;27(8):3969-82. DOI: 10.1109/TIP.2018.2830189
- dc.identifier.doi http://dx.doi.org/10.1109/TIP.2018.2830189
- dc.identifier.issn 1057-7149
- dc.identifier.uri http://hdl.handle.net/10230/47998
- dc.language.iso eng
- dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
- dc.relation.ispartof IEEE Transactions on Image Processing. 2018;27(8):3969-82
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/701236
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90124-P
- dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TIP.2018.2830189
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Mutiple instance learning
- dc.subject.keyword Undirected graphical models
- dc.subject.keyword Facial behavior analysis
- dc.subject.keyword Pain intensity
- dc.subject.keyword Action units
- dc.title Multi-instance dynamic ordinal random fields for weakly supervised dacial behavior analysis
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/acceptedVersion