Attention-Based Multimodal Fusion for Video Description

Understand

Currently successful methods for video description are based on encoder-decoder sentence generation using recur-rent neural networks (RNNs).

• Recent work has shown the advantage of integrating temporal and/or spatial attention mechanisms into these models, in which the decoder net-work predicts each word in the description by selectively giving more weight to encoded features from specific time frames (temporal attention) or to features from specific spatial regions (spatial attention).
• In this paper, we propose to expand the attention model to selectively attend not just to specific times or spatial regions, but to specific modalities of input such as image features, motion features, and audio features.
• Our new modality-dependent attention mechanism, which we call multimodal attention, provides a natural way to fuse multimodal information for video description.