Multimodal Learning

Meta-Transformer: A Unified Framework for Multimodal Learning – Unified Multimodal Learning. Meta-Transformer utilizes the same backbone to encode natural languages, images, point clouds, audio, video, infrared, hyperspectral, X-ray, time-series, tabular, and graph data. It reveals the potential of transformer architectures for universal perception. > Multimodal learning involves utilizing data from various modalities to improve model capacity. Despite the years of development in this field, it remains challenging to devise a unified framework for processing natural language, 2D images, 3D point clouds, and audio spectrograms due to crucial gaps among these different modalities. This study proposes a novel approach that demonstrates a network with frozen parameters can encode the data from the aforementioned four modalities and achieve favorable performance, resulting in a unified framework called Meta Transformer. Using this framework, the raw input data from various modalities are converted to a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks …