Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling
Authors: 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.