Using Aesthetics and Action Recognition-based Networks for the Prediction of Media Memorability
Mihai Gabriel Constantin, Kang Chen, Gabriela Dinu, Fréderic Dufaux, Giuseppe Valenzise, Bogdan Emanuel Ionescu · HAL (Le Centre pour la Communication Scientifique Directe) · 2019
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.