The proposed algorithms, Pixel Tracking and Flow Normalization, can indeed be extended to incorporate high-level semantic features from neural networks to further improve performance on specific event localization tasks. By integrating features extracted from pre-trained neural networks, such as ResNet or Transformer models, the algorithms can gain a deeper understanding of the content in the videos. These features can provide information about objects, actions, and scenes, enabling the algorithms to better identify specific events and boundaries. By combining motion cues with semantic features, the algorithms can achieve a more comprehensive understanding of the video content, leading to improved performance in event localization tasks.