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.

CLearViD是一种基于Transformer的视频描述生成模型，利用课程学习方法逐渐暴露模型于更具挑战性的样本和逐步降低网络容量，从而学习到更具鲁棒性和泛化性的特征，并利用Mish激活函数来缓解梯度消失问题。实验证明了该模型在ActivityNet Captions和YouCook2数据集上相对于现有的最先进模型在准确性和多样性指标上取得了显著的改善。

CLearViD: 视频描述的课程学习