Many real-world video-text tasks involve different levels of granularity to represent local and global information with distinct semantics, such as frames and words, clips and sentences, or videos and paragraphs. Most existing multimodal representation learning methods suffer from limitations: (i) Adopting expert systems or manual design to extract more fine-grained local information (such as objects and actions in a video frame) for supervision may lead to information asymmetry since there may no corresponding information among modalities; (ii) Neglecting the hierarchical nature of the data to aggregate different levels of information from different modalities will cause insufficient representations. To alleviate the above issues, in this paper, we propose a Multi-Granularity Aggregation Transformer (MGAT) for joint video-audio-text representation learning. Specifically, for intra-modality, we first design a multi-granularity transformer module to relieve information asymmetry by making full use of local and global information within a single modality from different perspectives. Then, for inter-modality, we develop an attention-guided aggregation module to fuse audio and video information hierarchically. Last, we align the aggregated information with text information at different hierarchical levels via intra- and inter-modality consistency loss and contrastive loss. With the help of more granularity of information, we are able to obtain a well-performed representation model for a variety of tasks, e.g., video-paragraph retrieval and video captioning. Extensive experiments on two challenging benchmarks, i.e., ActivityNet-captions and Youcook2, demonstrate the superiority of our proposed method.

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (e.g., students) to fully grasp this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (i.e., reading) the knowledge in a given text or generating (i.e., writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a Reader that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text Writer that generates a novel paragraph, and a Reviewer that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://github.com/menggehe/DRAW.