Video Captioning using LSTM-based Encoder-Decoder Architecture

This paper provides a way to improve video captioning by integrating the feature extraction capabilities of the VGG-16 Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) based Encoder-Decoder model. The suggested approach seeks to produce subtitles for videos that are both logical and pertinent to the context. Comprehensive visual features are retrieved from video frames by utilizing the VGG-16 model, which results in a comprehensive representation of visual content. Following the feeding of these features into an LSTM-based Encoder-Decoder architecture, the encoder processes the data while the decoder produces captions that provide context. The model can collect both spatial and temporal information thanks to the synergy between VGG-16 and LSTM, producing captions that are more visually appealing and contextually accurate. The outcome of the experiments shows how well this method works in generating high-quality video captions and how it can improve upon the state-of-the-art in video interpretation. This approach has potential for use in applications that need in-depth, complex video analysis.