The proposed temporal saliency prediction model, TempSAL, can be extended to handle dynamic scenes or videos by incorporating a temporal convolutional network (TCN) or a recurrent neural network (RNN) architecture that processes sequences of frames over time. This would allow the model to capture the temporal dependencies and transitions between frames, enabling it to predict saliency maps that reflect not only the static content of individual frames but also the motion and changes occurring in the scene.
To achieve this, the model could be modified to include a temporal feature extraction layer that aggregates information across multiple frames, effectively creating a spatiotemporal representation of the video. This could involve using 3D convolutions that operate over both spatial and temporal dimensions, or employing long short-term memory (LSTM) units to maintain a memory of previous frames, thus allowing the model to learn how attention shifts dynamically as the scene evolves.
Additionally, the model could benefit from integrating optical flow information to enhance the understanding of motion within the scene. By analyzing how objects move and interact over time, the model can better predict which areas will capture human attention in a video context. This extension would not only improve saliency prediction in videos but also facilitate applications in video summarization, object tracking, and scene understanding.