Title: VIDEO AFFECTIVE IMPACT PREDICTION WITH MULTIMODAL FUSION AND LONG-SHORT TEMPORAL CONTEXT. Abstract: Predicting the emotional impact of videos using machine learning is a challenging task. Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but
have not been successfully exploited. In this paper, we proposed a comprehensive framework with innovative designs of model structure and multi-modal fusion strategy. We select the most suitable modalities for valence and arousal tasks respectively and each modal feature is extracted using the modality-specific pre-trained deep model on large generic dataset. Two-time-scale structures, one for the intra-clip and the other for the inter-clip, are proposed to capture the temporal dependency of video content and emotional states. To combine the complementary information from multiple modalities, an effective and efficient residual-based progressive training strategy is proposed. Each modality is step-wisely combined into the
multi-modal model, responsible for completing the missing parts of features. With all those above, our proposed prediction framework achieves better performance with a large margin compared to the state-of-the-art.