Abstract:

In video captioning tasks， one of the common problems is that the object caption is not specific enough， mainly because the model does not fully learn the information of the objects in the video. Meanwhile， videos contain abundant feature information， such as object information， motion information， and contextual information， making it a challenging task to enhance the model’s ability to learn key information when generating captions. To address the aforementioned problems， this paper proposes a method based on enhanced object learning and attention networks. Firstly， a new enhanced object learning module was designed to fully learn object information in videos， thereby achieving accurate caption of video content. Secondly， an attention network was constructed to dynamically adjust the weights of different types of information， thereby enhancing the model’s ability to learn key information when generating captions. In the experiments on the MSVD and MSR-VTT datasets， the caption generated by the method proposed in this paper showed a higher level of specificity and accuracy， and exceeded the current advanced methods in various evaluation indicators， effectively verifying the feasibility of the method.

Key words: deep learning, video captioning, enhanced object learning, attention network