Efficient Video Captioning with Frame Similarity-Based Filtering

Video captioning combines computer vision and Natural Language Processing (NLP) to perform the challenging task of scene understanding. The rapid advancements in artificial intelligence have led to a growing interest in video captioning, which involves generating natural language descriptions based on the visual content of videos. In this paper, we present a novel approach to video caption generation. The proposed method first extracts frames from the video and reduces the number of frames based on their similarity. The remaining frames are then processed by a Convolution Neural Network (CNN) to extract a feature vector, which is then fed into a Long Short-Term Memory (LSTM) network to generate the captions. The results are compared with the state-of-the-art models which demonstrate that the proposed approach outperforms the existing methods on MSVD, M-VAD, and MPII-MD datasets.