To extend the proposed model for handling long-range temporal dependencies in video action detection, several strategies could be implemented. One approach is to incorporate recurrent neural networks (RNNs) or long short-term memory (LSTM) networks into the architecture. These networks are designed to capture temporal relationships over extended sequences, allowing the model to maintain context across longer video clips.
Another method is to utilize attention mechanisms that specifically focus on temporal relationships, such as Temporal Self-Attention or Temporal Convolutional Networks (TCNs). By applying these techniques, the model can learn to weigh the importance of frames over longer intervals, effectively capturing the dynamics of actions that unfold over time.
Additionally, integrating a hierarchical structure that processes video clips at multiple temporal resolutions could enhance the model's ability to capture both short-term and long-term dependencies. This could involve using a multi-scale approach where different layers of the model focus on varying temporal spans, allowing for a more comprehensive understanding of the action context.
Lastly, leveraging transformer architectures with enhanced positional encoding that accounts for temporal information could also be beneficial. By modifying the positional embeddings to reflect the temporal aspect of the video data, the model can better understand the sequence of actions and their relationships over time.