Multimodal AI involves the integration and analysis of multiple data modalities, such as text, images, audio, and video, to enable machines to perceive, understand, and interact with the world in a more human-like manner.
In the 1990s researchers began exploring the integration of multiple modalities, driven by the increasing availability of digital data and advances in computer vision and speech recognition technologies. The following key developments underpin today’s multi-modal models:
- Deep Learning and Neural Networks: The advent of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), revolutionized multimodal AI research. These models excel at processing and extracting meaningful representations from various types of data, enabling improved performance in tasks like object recognition, speech synthesis, and machine translation.
- Transfer Learning and Pretraining: Transfer learning has played a vital role in multimodal AI by leveraging pretraining on large-scale datasets, such as ImageNet and COCO, to extract general-purpose features. Pretrained models like OpenAI's CLIP and Facebook's ViT have demonstrated impressive capabilities in understanding and generating text-image associations, bridging the semantic gap between different modalities.
- Attention Mechanisms: Attention mechanisms have emerged as a fundamental component in multimodal AI architectures. By allowing the model to focus on specific regions or parts of data, attention mechanisms enhance the understanding and alignment of multimodal inputs, enabling more accurate and context-aware predictions.
However combining and fusing data from multiple modalities is a non-trivial task due to the inherent differences in data structures, scales, and temporal characteristics. Developing effective fusion strategies that preserve the complementary information of each modality is a challenge that requires careful consideration.
