Semi Supervised Meta Learning for Spatiotemporal Learning
Faraz Waseem Pratyush Muthukumar
We approached the goal of applying meta-learning to self-supervised masked
autoencoders for spatiotemporal learning in three steps. Broadly, we seek to
understand the impact of applying meta-learning to existing state-of-the-art
representation learning architectures. Thus, we test spatiotemporal learning
through: a meta-learning architecture only, a representation learning
architecture only, and an architecture applying representation learning
alongside a meta learning architecture. We utilize the Memory Augmented Neural
Network (MANN) architecture to apply meta-learning to our framework.
Specifically, we first experiment with applying a pre-trained MAE and
fine-tuning on our small-scale spatiotemporal dataset for video reconstruction
tasks. Next, we experiment with training an MAE encoder and applying a
classification head for action classification tasks. Finally, we experiment
with applying a pre-trained MAE and fine-tune with MANN backbone for action
classification tasks.