MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are…

• FD
  Fundação de Amparo à Pesquisa do Estado de São Paulo

  Award: 62176038,62001450,U1903215