While the masked modeling approach used in the Social-MAE framework offers several advantages, it also has limitations when capturing long-term dependencies and complex social dynamics in multi-person scenes:
Limited Context: Masked modeling focuses on reconstructing masked tokens based on the unmasked context, which may limit the model's ability to capture long-term dependencies that span across multiple time steps. This can result in a shallow understanding of the underlying dynamics in the data.
Temporal Information Loss: Masked modeling may lead to information loss over long sequences, especially when the masked tokens contain critical temporal dependencies. This can impact the model's ability to accurately predict future states or understand complex social interactions that evolve over time.
Difficulty in Modeling Interactions: Complex social dynamics in multi-person scenes involve intricate interactions between individuals, which may not be fully captured by the masked modeling approach. The model may struggle to learn the nuanced relationships and dependencies that exist in social contexts.
Overfitting to Masked Patterns: The model may inadvertently overfit to the patterns present in the masked tokens during pre-training, leading to a biased representation that may not generalize well to unseen data or complex social scenarios.
To mitigate these limitations, it is essential to complement the masked modeling approach with other techniques that can capture long-term dependencies and complex social dynamics effectively. This may include incorporating attention mechanisms, recurrent connections, or hierarchical structures in the model architecture to enhance its ability to understand and predict social interactions in multi-person scenes.