Multimodal Learning: Bridging the Senses of AI
In our everyday lives, we effortlessly weave together vision, sound, touch, language, and even spatial awareness to form a rich, unified understanding of the world. When you enter a new room, you don’t just see the furniture—you hear echoes, feel textures, read labels, and instinctively map the space in three dimensions. This seamless fusion of sensory data empowers humans to reason flexibly, adapt to novel situations, and make nuanced decisions.
Drawing inspiration from this human faculty, we build multimodal representation learning systems. Rather than processing images, text, audio, or 3D data in isolation, we train models to ingest and align multiple modalities into a shared latent space. By doing so, our algorithms learn the complementary strengths of each signal—visual cues disambiguate spoken commands, linguistic context enriches scene understanding, and 3D geometry grounds tactile or spatial reasoning.