FEATURE ENHANCEMENT AND FUSION FOR RGB-T SALIENT OBJECT DETECTION

Cross-modal information fusion plays a vital role in the RGB-T salient object detection. Due to RGB and thermal images come from different domains, the modality difference will lead to the unsatisfactory effect of simple feature fusion. How to explore and integrate useful information is the key to the RGB-T saliency detection methods. In this paper, we introduce an Enhancement and Fusion Network. In detail, we propose a Self-modality Feature Enhancement Module that effectively integrate the feature representation of a single modality through global context information. And we propose a Cross-modality Feature Dynamic Fusion Module to realize the effective fusion of cross-modal features in the way of dynamic weighting. Experiments on public datasets show that the proposed method achieves satisfactory results compared with other state-of-the-art salient object detection approaches.