Marginalized Average Attentional Network for Weakly-Supervised Learning

Yuan Yuan; Yueming Lyu; Xi Shen; Ivor Tsang; Dit-Yan Yeung

Marginalized Average Attentional Network for Weakly-Supervised Learning

Yuan Yuan ¹ Yueming Lyu ² Xi Shen ³ Ivor Tsang ⁴ Dit-Yan Yeung ⁵

1 INOCS - Integrated Optimization with Complex Structure

ULB - Université Libre de Bruxelles [Bruxelles], Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

2 UTS - University of Technology Sydney

3 IMAGINE [Marne-la-Vallée]

LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech

4 Alcatel-Lucent Bell - Belgique

5 HKUST - Hong Kong University of Science and Technology

Abstract : In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2 T) to O(T 2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization.

Type de document :

Communication dans un congrès

Domaine :

Informatique [cs] / Intelligence artificielle [cs.AI]

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https://hal-enpc.archives-ouvertes.fr/hal-02057597
Contributeur : Xi Shen <>
Soumis le : mardi 5 mars 2019 - 14:20:47
Dernière modification le : lundi 8 avril 2019 - 14:27:31
Document(s) archivé(s) le : jeudi 6 juin 2019 - 17:53:35

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