Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection

• Main Authors: Gao, Wei, Liao, Guibiao, Ma, Siwei, Li, Ge, Liang, Yongsheng, Lin, Weisi
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Dynamic Cross-Modal Guided Mechanism; RGBD/RGB-T Multi-Modal Data
• Online Access: https://hdl.handle.net/10356/150772
• Institution: Nanyang Technological University

The use of complementary information, namely depth or thermal information, has shown its benefits to salient object detection (SOD) during recent years. However, the RGB-D or RGB-T SOD problems are currently only solved independently, and most of them directly extract and fuse raw features from backbones. Such methods can be easily restricted by low-quality modality data and redundant cross-modal features. In this work, a unified end-to-end framework is designed to simultaneously analyze RGB-D and RGB-T SOD tasks. Specifically, to effectively tackle multi-modal features, we propose a novel multi-stage and multi-scale fusion network (MMNet), which consists of a crossmodal multi-stage fusion module (CMFM) and a bi-directional multi-scale decoder (BMD). Similar to the visual color stage doctrine in the human visual system (HVS), the proposed CMFM aims to explore important feature representations in feature response stage, and integrate them into cross-modal features in adversarial combination stage. Moreover, the proposed BMD learns the combination of multi-level cross-modal fused features to capture both local and global information of salient objects, and can further boost the multi-modal SOD performance. The proposed unified cross-modality feature analysis framework based on two-stage and multi-scale information fusion can be used for diverse multi-modal SOD tasks. Comprehensive experiments (∼92K image-pairs) demonstrate that the proposed method consistently outperforms the other 21 state-of-the-art methods on nine benchmark datasets. This validates that our proposed method can work well on diverse multi-modal SOD tasks with good generalization and robustness, and provides a good multimodal SOD benchmark.