Label Importance Ranking with Entropy Variation Complex Networks for Structured Video Captioning
Wenjia Tian, Yanzhu Hu · Traitement du signal · 2021
Structured video captioning is a fundamental yet challenging task in both computer vision and artificial intelligence (AI).The prevalent approach is to map an input video to a variablelength output sentence with models like recurrent neural network (RNN).This paper presents a new model based on an improved scene-aware bidirectional long short-term memory network (SABi-LSTM), and names the model as label importance ranking with entropy variation complex networks of structured video captions.Structured video captioning is a three-level structured system, including a multi-feature fusion level, an SABi-LSTM level, and a label importance ranking level.The system decomposes structures of multiple levels and dimensions from different perspectives to perform video captioning.This work affirms the theoretical and practical significance of label importance ranking to video caption generation, and regards entropy as a local level metric to quantify label importance.Hence, entropy variation was proposed to define label importance, namely, the variation of the network entropy through label removal.It is assumed that the removal of an important label could cause sustainable variation to the structure.Hence, the authors defined the label importance ranking with entropy variation complex network algorithm to calculate the weight model of label nodes marked by video, and obtain the final caption of the video.Empirical results on Microsoft Video Caption (MSVD) dataset and MSR-Video to Text (MSR-VTT) dataset demonstrate the superiority of our approach for structured video captioning, especially on MSVD dataset.