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
3 IMAGINE [Marne-la-Vallée]
ENPC - École des Ponts ParisTech, LIGM - Laboratoire d'Informatique Gaspard-Monge
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.
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Soumis le : mardi 5 mars 2019 - 14:20:47
Dernière modification le : mardi 19 novembre 2019 - 16:45:16
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  • HAL Id : hal-02057597, version 1


Yuan Yuan, Yueming Lyu, Xi Shen, Ivor Tsang, Dit-Yan Yeung. Marginalized Average Attentional Network for Weakly-Supervised Learning. ICLR 2019 - Seventh International Conference on Learning Representations, May 2019, New-Orleans, United States. ⟨hal-02057597⟩



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