Aiming to advance AI agents, large foundation models significantly improve
reasoning and instruction execution, yet the current focus on vision and
language neglects the potential of perceiving diverse modalities in open-world
environments. However, the success of data-driven vision and language models is
costly or even infeasible to be reproduced for rare modalities. In this paper,
we present ViT-Lens-2 that facilitates efficient omni-modal representation
learning by perceiving novel modalities with a pretrained ViT and aligning them
to a pre-defined space. Specifically, the modality-specific lens is tuned to
project any-modal signals to an intermediate embedding space, which are then
processed by a strong ViT with pre-trained visual knowledge. The encoded
representations are optimized toward aligning with the modal-independent space,
pre-defined by off-the-shelf foundation models. ViT-Lens-2 provides a unified
solution for representation learning of increasing modalities with two
appealing advantages: (i) Unlocking the great potential of pretrained ViTs to
novel modalities effectively with efficient data regime; (ii) Enabling emergent
downstream capabilities through modality alignment and shared ViT parameters.
We tailor ViT-Lens-2 to learn representations for 3D point cloud, depth, audio,
tactile and EEG, and set new state-of-the-art results across various
understanding tasks, such as zero-shot classification. By seamlessly
integrating ViT-Lens-2 into Multimodal Foundation Models, we enable
Any-modality to Text and Image Generation in a zero-shot manner. Code and
models are available at this https URL

通过使用预训练的 ViT 和对齐模态，ViT-Lens-2 提供了一种有效的方法来探索新颖模态的各种新颖任务，并在各种理解任务中取得了新的最佳结果，包括零样本分类。