Lightweight Cross-Modal Representation Learning
This addresses the need for low-cost cross-modal learning for applications handling diverse modalities like text, audio, images, and video, though it appears incremental as it builds on existing representation learning methods.
The paper tackles the problem of high resource and time costs in cross-modal representation learning by introducing LightCRL, which uses a Deep Fusion Encoder to project multiple modalities into a shared latent space, reducing parameters while maintaining robust performance comparable to complex systems.
Low-cost cross-modal representation learning is crucial for deriving semantic representations across diverse modalities such as text, audio, images, and video. Traditional approaches typically depend on large specialized models trained from scratch, requiring extensive datasets and resulting in high resource and time costs. To overcome these challenges, we introduce a novel approach named Lightweight Cross-Modal Representation Learning (LightCRL). This method uses a single neural network titled Deep Fusion Encoder (DFE), which projects data from multiple modalities into a shared latent representation space. This reduces the overall parameter count while still delivering robust performance comparable to more complex systems.