The Context-Aware Interaction Network (CAINet) is proposed for RGB-T semantic segmentation to address the issue of ineffective exploration of complementary relationships between modalities in existing methods. CAINet constructs an interaction space to exploit auxiliary tasks and global context for explicit learning. It introduces the Context-Aware Complementary Reasoning (CACR) module to establish complementary relationships between multimodal features using long-term context in spatial and channel dimensions. The Global Context Modeling (GCM) module provides global context for feature interactions, while the Detail Aggregation (DA) module refines segmentation maps by aggregating detailed features. Auxiliary supervision is used to explicitly guide context interaction and improve feature representation. Extensive experiments on MFNet and PST900 datasets show that CAINet achieves state-of-the-art performance. The code is available at https://github.com/YingLv1106/CAINet. The main contributions include exploring the complementary relationship between multimodal features, proposing CACR, GCM, and DA modules, and introducing auxiliary supervision and residual learning. CAINet outperforms existing methods on MFNet and PST900 datasets, demonstrating its effectiveness in RGB-T semantic segmentation. The model also shows strong performance on RGB-D data, indicating its applicability to various datasets. Future work will focus on lightweight algorithms for embedded platforms.The Context-Aware Interaction Network (CAINet) is proposed for RGB-T semantic segmentation to address the issue of ineffective exploration of complementary relationships between modalities in existing methods. CAINet constructs an interaction space to exploit auxiliary tasks and global context for explicit learning. It introduces the Context-Aware Complementary Reasoning (CACR) module to establish complementary relationships between multimodal features using long-term context in spatial and channel dimensions. The Global Context Modeling (GCM) module provides global context for feature interactions, while the Detail Aggregation (DA) module refines segmentation maps by aggregating detailed features. Auxiliary supervision is used to explicitly guide context interaction and improve feature representation. Extensive experiments on MFNet and PST900 datasets show that CAINet achieves state-of-the-art performance. The code is available at https://github.com/YingLv1106/CAINet. The main contributions include exploring the complementary relationship between multimodal features, proposing CACR, GCM, and DA modules, and introducing auxiliary supervision and residual learning. CAINet outperforms existing methods on MFNet and PST900 datasets, demonstrating its effectiveness in RGB-T semantic segmentation. The model also shows strong performance on RGB-D data, indicating its applicability to various datasets. Future work will focus on lightweight algorithms for embedded platforms.