  During recent years transformers architectures have been growing in
popularity. Modulated Detection Transformer (MDETR) is an end-to-end
multi-modal understanding model that performs tasks such as phase grounding,
referring expression comprehension, referring expression segmentation, and
visual question answering. One remarkable aspect of the model is the capacity
to infer over classes that it was not previously trained for. In this work we
explore the use of MDETR in a new task, action detection, without any previous
training. We obtain quantitative results using the Atomic Visual Actions
dataset. Although the model does not report the best performance in the task,
we believe that it is an interesting finding. We show that it is possible to
use a multi-modal model to tackle a task that it was not designed for. Finally,
we believe that this line of research may lead into the generalization of MDETR
in additional downstream tasks.
