  In recent years, advancements in representation learning and language models
have propelled Automated Captioning (AC) to new heights, enabling the
generation of human-level descriptions. Leveraging these advancements, we
propose AVCap, an Audio-Visual Captioning framework, a simple yet powerful
baseline approach applicable to audio-visual captioning. AVCap utilizes
audio-visual features as text tokens, which has many advantages not only in
performance but also in the extensibility and scalability of the model. AVCap
is designed around three pivotal dimensions: the exploration of optimal
audio-visual encoder architectures, the adaptation of pre-trained models
according to the characteristics of generated text, and the investigation into
the efficacy of modality fusion in captioning. Our method outperforms existing
audio-visual captioning methods across all metrics and the code is available on
https://github.com/JongSuk1/AVCap
