Feature fusion refers to the process of integrating derivative data features within a neural network. This technique aims to combine multi-source or multi-level features (such as those from different layers or modalities) to enhance the representational capability of the model, enabling it to capture more comprehensive and discriminative information for tasks like image recognition or semantic segmentation.
