  The transformer neural network architecture allows for autoregressive
sequence-to-sequence modeling through the use of attention layers. It was
originally created with the application of machine translation but has
revolutionized natural language processing. Recently, transformers have also
been applied across a wide variety of pattern recognition tasks, particularly
in computer vision. In this literature review, we describe major advances in
computer vision utilizing transformers. We then focus specifically on
Multi-Object Tracking (MOT) and discuss how transformers are increasingly
becoming competitive in state-of-the-art MOT works, yet still lag behind
traditional deep learning methods.
