  Video description entails automatically generating coherent natural language
sentences that narrate the content of a given video. We introduce CLearViD, a
transformer-based model for video description generation that leverages
curriculum learning to accomplish this task. In particular, we investigate two
curriculum strategies: (1) progressively exposing the model to more challenging
samples by gradually applying a Gaussian noise to the video data, and (2)
gradually reducing the capacity of the network through dropout during the
training process. These methods enable the model to learn more robust and
generalizable features. Moreover, CLearViD leverages the Mish activation
function, which provides non-linearity and non-monotonicity and helps alleviate
the issue of vanishing gradients. Our extensive experiments and ablation
studies demonstrate the effectiveness of the proposed model. The results on two
datasets, namely ActivityNet Captions and YouCook2, show that CLearViD
significantly outperforms existing state-of-the-art models in terms of both
accuracy and diversity metrics.

Computer Vision and Pattern Recognition;Computation and Language;Machine Learning