Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos
This addresses the challenge of bridging language and videos for tasks like video captioning and localization, with incremental improvements over existing methods.
The paper tackled the problem of generating event captions and temporally localizing sentences in videos by proposing a method to learn pairwise modality interactions, achieving state-of-the-art performances on four benchmark datasets.
Automatically generating sentences to describe events and temporally localizing sentences in a video are two important tasks that bridge language and videos. Recent techniques leverage the multimodal nature of videos by using off-the-shelf features to represent videos, but interactions between modalities are rarely explored. Inspired by the fact that there exist cross-modal interactions in the human brain, we propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos and thus improve performances on both tasks. We model modality interaction in both the sequence and channel levels in a pairwise fashion, and the pairwise interaction also provides some explainability for the predictions of target tasks. We demonstrate the effectiveness of our method and validate specific design choices through extensive ablation studies. Our method turns out to achieve state-of-the-art performances on four standard benchmark datasets: MSVD and MSR-VTT (event captioning task), and Charades-STA and ActivityNet Captions (temporal sentence localization task).