  This research paper introduces an innovative AI coaching approach by
integrating vision-encoder-decoder models. The feasibility of this method is
demonstrated using a Vision Transformer as the encoder and GPT-2 as the
decoder, achieving a seamless integration of visual input and textual
interaction. Departing from conventional practices of employing distinct models
for image recognition and text-based coaching, our integrated architecture
directly processes input images, enabling natural question-and-answer dialogues
with the AI coach. This unique strategy simplifies model architecture while
enhancing the overall user experience in human-AI interactions. We showcase
sample results to demonstrate the capability of the model. The results
underscore the methodology's potential as a promising paradigm for creating
efficient AI coach models in various domains involving visual inputs.
Importantly, this potential holds true regardless of the particular visual
encoder or text decoder chosen. Additionally, we conducted experiments with
different sizes of GPT-2 to assess the impact on AI coach performance,
providing valuable insights into the scalability and versatility of our
proposed methodology.
