Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object Detection

While significant progress has been made in recent years in the field of salient object detection (SOD), there are still limitations in heterogeneous modality fusion and salient feature integrity learning. The former is primarily attributed to a paucity of attention from researchers to the fusion of cross-scale information between different modalities during processing multi-modal heterogeneous data, coupled with an absence of methods for adaptive control of their respective contributions. The latter constraint stems from the shortcomings in existing approaches concerning the prediction of salient region’s integrity. To address these problems, we propose a Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object Detection, denoted as HFIL-Net. In response to the first challenge, we design an Advanced Semantic Guidance Aggregation (ASGA) module, which utilizes three fusion blocks to achieve the aggregation of three types of information: within-scale cross-modal, within-modal cross-scale, and cross-modal cross-scale. In addition, we embed the local fusion factor matrices in the ASGA module and utilize the global fusion factor matrices in the Multi-modal Information Adaptive Fusion (MIAF) module to control the contributions adaptively from different perspectives during the fusion process. For the second issue, we introduce the Feature Integrity Learning and Refinement (FILR) Module. It leverages the idea of ”part-whole” relationships from capsule networks to learn feature integrity and further refine the learned features through attention mechanisms. Extensive experimental results demonstrate that our proposed HFIL-Net outperforms over 17 state-of-the-art (SOTA) detection methods in testing across seven challenging standard datasets. Codes and results are available on https://github.com/BojueGao/HFIL-Net.