Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning
This is an incremental improvement for video captioning tasks, focusing on model architecture adjustments to enhance grammar and correctness.
The paper tackles video captioning by implementing an encoder-decoder LSTM model to map video frames to text captions, achieving evaluation with BLEU scores and demonstrating generalization over video actions and scene changes.
This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.