An Image is Worth 16X16 Words:
TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE
A Transformer consists of alternating layers of multiheaded self-attention.
The Transformer Paper adapts a NLP architecture for making Image Classification. For that, it first need to tokenize the image (like a piece of text). The tokenization is done by splitting the image into fixed-size patches and then embedding it.
In other words, they treat a picture as a set of sub-images, just like a phrase is a set of words.
Relational Deep Reinforcement Learning
The Reinforcement Learning paper uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy.
Focused on game playing, they decide to model a non-local concept of entities (players, objects) and the relationship between them.
So instead of tokenizing the image, they first extract entities from it, using a more standard approach, which is convolution. So the multi-head attention layer receives a set of entities, the output is normalized so it's once again formatted as the entity set, so it can proceed with an iterative process.
TL;DR: Same base (very promising) technique, but applied to 2 drastically different models.