The SGP-Mixer layer, while effective in enhancing token discriminability and aggregating information from different temporal scales, may have limitations in capturing long-range temporal dependencies due to:
Limited Contextual Information: The SGP-Mixer layer may struggle to capture dependencies that span a large number of frames, as it primarily focuses on local interactions within a sequence.
Information Propagation: Information propagation across multiple layers of the SGP-Mixer may lead to vanishing or exploding gradients, hindering the model's ability to capture long-range dependencies effectively.
To address these limitations, the SGP-Mixer layer can be enhanced by:
Incorporating Attention Mechanisms: Introducing self-attention mechanisms within the SGP-Mixer layer can help the model capture long-range dependencies by allowing it to attend to relevant temporal contexts across the sequence.
Residual Connections: Adding residual connections within the SGP-Mixer layer can facilitate the flow of information and gradients, enabling the model to capture dependencies over longer temporal ranges.
LSTM or GRU Integration: Integrating LSTM or GRU units within the SGP-Mixer layer can provide the model with the ability to retain and update information over extended temporal contexts, improving its long-range dependency modeling capabilities.