Token Turing Machines
This work addresses efficient processing of long visual sequences for applications like video analysis and robotics, representing an incremental improvement with a novel memory mechanism.
The authors tackled the problem of real-world sequential visual understanding by proposing Token Turing Machines (TTM), a Transformer model with external memory, which outperformed other models like long-sequence Transformers and RNNs on tasks such as online temporal activity detection and vision-based robot action policy learning.
We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history (i.e., frames). This memory is efficiently addressed, read and written using a Transformer as the processing unit/controller at each step. The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step. We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential visual understanding tasks: online temporal activity detection from videos and vision-based robot action policy learning. Code is publicly available at: https://github.com/google-research/scenic/tree/main/scenic/projects/token_turing