Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning
This addresses the problem of multi-modal fusion in video captioning for AI systems, representing an incremental improvement with novel architectural components.
The paper tackles the challenge of combining audio and visual cues for video captioning by proposing a hierarchically aligned cross-modal attention (HACA) framework that learns and selectively fuses global and local temporal dynamics of different modalities, achieving new state-of-the-art results on the MSR-VTT dataset.
A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.