Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense
  Interactions through Masked Modeling
Shentong Mo Pedro Morgado
Humans possess a remarkable ability to integrate auditory and visual
information, enabling a deeper understanding of the surrounding environment.
This early fusion of audio and visual cues, demonstrated through cognitive
psychology and neuroscience research, offers promising potential for developing
multimodal perception models. However, training early fusion architectures
poses significant challenges, as the increased model expressivity requires
robust learning frameworks to harness their enhanced capabilities. In this
paper, we address this challenge by leveraging the masked reconstruction
framework, previously successful in unimodal settings, to train audio-visual
encoders with early fusion. Additionally, we propose an attention-based fusion
module that captures interactions between local audio and visual
representations, enhancing the model's ability to capture fine-grained
interactions. While effective, this procedure can become computationally
intractable, as the number of local representations increases. Thus, to address
the computational complexity, we propose an alternative procedure that
factorizes the local representations before representing audio-visual
interactions. Extensive evaluations on a variety of datasets demonstrate the
superiority of our approach in audio-event classification, visual sound
localization, sound separation, and audio-visual segmentation. These
contributions enable the efficient training of deeply integrated audio-visual
models and significantly advance the usefulness of early fusion architectures.