HEFNet: Hierarchical Unimodal Enhancement and Multi-modal Fusion for RGB-T Salient Object Detection

RGB-Thermal salient object detection (RGB-T SOD) aims to identify and segment visually prominent objects by leveraging complementary information from RGB and thermal modalities. A key challenge lies in exploiting both the uniqueness and shared characteristics of these modalities to enhance their collaboration. Existing methods often ignore the optimization of unimodal features and the level-specific modality discrepancy, leading to noisy and redundant multi-modal feature representations. To address these limitations, we propose a novel RGB-T SOD network, HEFNet, which employs hierarchical unimodal enhancement and multi-modal fusion to achieve precise segmentation. Specifically, we introduce the unimodal feature enhancement (UFE) module, which refines RGB and thermal features by incorporating complementary information from adjacent levels, thereby enhancing saliency cues and suppressing noise distractions. Additionally, the hierarchical multi-modal fusion (HMF) module is designed to generate robust cross-modal feature representation. By employing tailored refinement and fusion strategies within the UFE and HMF modules, our network fully exploits the strengths of each modality, facilitating the generation of discriminative cross-modal features. Finally, the multi-level feature integration (MFI) module is introduced to progressively aggregate features across levels to ensure accurate saliency predictions. Extensive experiments demonstrate that our method achieves state-of-the-art performance, verifying its effectiveness and superiority over existing RGB-T SOD approaches. Our results are available at https://github.com/ZhangQing0329/HEFNet