Retrieving and Highlighting Action with Spatiotemporal Reference
This addresses the fine-grained task of action highlighting for video analysis, which is incremental over existing methods.
The paper tackles the problem of jointly retrieving and spatiotemporally highlighting actions in untrimmed videos by enhancing deep cross-modal retrieval methods, resulting in a 2-3% improvement in retrieval recall on the MSR-VTT dataset.
In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which relate to the nouns and verbs in captions. Through experiments, we show that our model generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map. Also, our model improves retrieval recall over our baseline without alignment by 2-3% on the MSR-VTT dataset.