Early fusion

Early fusion, or feature-level fusion, combines raw data or initial feature vectors from multiple modalities (e.g., image, audio) into a single, high-dimensional representation *before* the primary deep learning model begins processing.

This strategy is a foundational approach in multimodal AI, executed by merging input data streams at the earliest stage of the pipeline. The most common technique is vector concatenation: for example, joining a 1024-dimension image feature vector with a 512-dimension text vector to create a single 1536-dimension input. This forces the network to learn low-level, intricate correlations between modalities from the first layer, often improving robustness in noisy environments. Applications are critical in fields like autonomous vehicles (fusing LiDAR point clouds and camera pixels) and multimodal sentiment analysis (combining text, audio, and visual cues). The benefit is a simplified, single-model training process, though it risks high dimensionality and requires precise data alignment.

https://apxml.com/multimodal-ai-fusion-strategies/