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Communication Dans Un Congrès Année : 2019

Using Aesthetics and Action Recognition-based Networks for the Prediction of Media Memorability

Résumé

In this working note paper we present the contribution and results of the participation of the UPB-L2S team to the MediaEval 2019 Predicting Media Memorability Task. The task requires participants to develop machine learning systems able to predict automatically whether a video will be memorable for the viewer, and for how long (e.g., hours, or days). To solve the task, we investigated several aesthetics and action recognition-based deep neural networks, either by fine-tuning models or by using them as pre-trained feature extractors. Results from different systems were aggregated in various fusion schemes. Experimental results are positive showing the potential of transfer learning for this tasks.
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Dates et versions

hal-02368920 , version 1 (10-01-2020)

Identifiants

  • HAL Id : hal-02368920 , version 1

Citer

Mihai Gabriel Constantin, Chen Kang, Gabriela Dinu, Frédéric Dufaux, Giuseppe Valenzise, et al.. Using Aesthetics and Action Recognition-based Networks for the Prediction of Media Memorability. MediaEval 2019 Workshop, Oct 2019, Sophia Antipolis, France. ⟨hal-02368920⟩
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