Video captioning based on vision transformer and reinforcement learning.

ABSTRACT: Global encoding of visual features in video captioning is important for improving the description accuracy. In this paper, we propose a video captioning method that combines Vision Transformer (ViT) and reinforcement learning. Firstly, Resnet-152 and ResNeXt-101 are used to extract features from videos. Secondly, the encoding block of the ViT network is applied to encode video features. Thirdly, the encoded features are fed into a Long Short-Term Memory (LSTM) network to generate a video content description. Finally, the accuracy of video content description is further improved by fine-tuning reinforcement learning. We conducted experiments on the benchmark dataset MSR-VTT used for video captioning. The results show that compared with the current mainstream methods, the model in this paper

SUBMITTER: Zhao H 

PROVIDER: S-EPMC9044334 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature