To enable the pre-trained model to effectively leverage segment-level annotations in the cross-distribution dataset for improved learning of scale variance, future research could focus on developing semi-supervised or weakly-supervised learning strategies that incorporate segment-level annotations as auxiliary information. One approach could involve designing a multi-task learning framework where the model simultaneously learns from segment-level annotations and video-level labels during training.
Furthermore, leveraging self-supervised learning techniques, such as contrastive learning or temporal pretext tasks, could help the model extract more informative features from segment-level annotations without the need for explicit supervision. By encouraging the model to learn meaningful representations from the segment-level data, it can better adapt to scale variance and other challenges present in cross-distribution datasets.
Additionally, active learning strategies could be employed to selectively query segment-level annotations for the most informative samples, optimizing the model's learning process and reducing the overall annotation cost. By intelligently incorporating segment-level annotations into the training pipeline and exploring innovative learning paradigms, the pre-trained model can effectively leverage such annotations for improved learning of scale variance in cross-distribution datasets.