In the expanding field of video understanding, the ability to distill video content into a coherent summary is a valuable asset, not only for assisting users in rapidly consuming relevant portions of lengthy videos but also for enabling more sophisticated video indexing and search functionalities. This significance is particularly pronounced in the context of news videos, where the accurate identification of specific people, places, and organizations—collectively known as named entities—is pivotal for producing meaningful captions that reflect the essence of the video content.
Addressing this challenge, a novel approach to video summarization has been introduced, which puts a spotlight on generating captions that are aware of and incorporate named entities, contrasting the typical generic descriptions seen in most video captioning tasks. To foster research in this specialized summarization task, a new dataset termed VIEWS (VIdeo NEWS) has been released. This dataset is large-scale and features news videos paired with rich, entity-focused captions, validated for high alignment with the video content it describes.
The proposed method takes an innovative approach to video captioning; it first deploys an Entity Perceiver to directly identify named entities from the video. It then leverages a Knowledge Retriever that mines external world knowledge using these detected entities to provide contextual insight. Finally, a cutting-edge captioning model integrates this information, generating entity-aware captions that encapsulate the video's key content.
Rigorous experimentation was carried out to ascertain the efficacy of the proposed approach in enhancing the video captioning task. Using three different state-of-the-art video captioning models, it was shown that integrating external knowledge and entity recognition substantially improves the quality of the generated video captions in comparison to models that rely solely on visual information. Furthermore, when applied to an existing news image-captioning dataset, the proposed approach demonstrated impressive generalization capabilities, indicating its potential adaptability across various types of textual-visual content.
The insights gained from a series of detailed ablation studies point to interesting opportunities for future research. For instance, improving the recognition of named entities has the potential to markedly amplify the performance gains in entity-aware video captioning. As such, this paper establishes a robust foundation for ongoing exploration in the field of entity-aware video summarization, presenting exciting avenues for the development of more nuanced and informative video captioning solutions.