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During recent years transformers architectures have been growing inpopularity. Modulated Detection Transformer (MDETR) is an end-to-endmulti-modal understanding model that performs tasks such as phase grounding,referring expression comprehension, referring expression segmentation, andvisual question answering. One remarkable aspect of the model is the capacityto infer over classes that it was not previously trained for. In this work weexplore the use of MDETR in a new task, action detection, without any previoustraining. We obtain quantitative results using the Atomic Visual Actionsdataset. 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 touse 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 MDETRin additional downstream tasks.

Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos