This paper proposes a novel Spatial and Channel Synergistic Attention (SCSA) module to explore the synergistic effects between spatial and channel attention mechanisms. The SCSA module consists of two components: the Shareable Multi-Semantic Spatial Attention (SMSA) and the Progressive Channel-wise Self-Attention (PCSA). SMSA integrates multi-semantic information and utilizes a progressive compression strategy to inject discriminative spatial priors into PCSA's channel self-attention, effectively guiding channel recalibration. PCSA further mitigates semantic disparities among different sub-features through robust feature interactions based on self-attention. The SCSA module is evaluated on seven benchmark datasets, including classification on ImageNet-1K, object detection on MSCOCO 2017, and segmentation on ADE20K. The results show that SCSA outperforms existing attention mechanisms in terms of accuracy and generalization across various tasks. The code and models are available at https://github.com/HZAI-ZJNU/SCSA. The SCSA module is designed to enhance feature extraction by leveraging multi-semantic spatial information and mitigating semantic disparities through dimension decoupling, lightweight multi-semantic guidance, and semantic disparities mitigation. The experiments demonstrate that SCSA achieves superior performance in image classification, object detection, and semantic segmentation tasks. The SCSA module is also validated on complex scene detection datasets, showing its effectiveness in handling challenging scenarios such as small targets, dark environments, and infrared scenes. The results indicate that SCSA provides a practical trade-off between computational complexity and performance, making it a promising approach for various vision tasks.This paper proposes a novel Spatial and Channel Synergistic Attention (SCSA) module to explore the synergistic effects between spatial and channel attention mechanisms. The SCSA module consists of two components: the Shareable Multi-Semantic Spatial Attention (SMSA) and the Progressive Channel-wise Self-Attention (PCSA). SMSA integrates multi-semantic information and utilizes a progressive compression strategy to inject discriminative spatial priors into PCSA's channel self-attention, effectively guiding channel recalibration. PCSA further mitigates semantic disparities among different sub-features through robust feature interactions based on self-attention. The SCSA module is evaluated on seven benchmark datasets, including classification on ImageNet-1K, object detection on MSCOCO 2017, and segmentation on ADE20K. The results show that SCSA outperforms existing attention mechanisms in terms of accuracy and generalization across various tasks. The code and models are available at https://github.com/HZAI-ZJNU/SCSA. The SCSA module is designed to enhance feature extraction by leveraging multi-semantic spatial information and mitigating semantic disparities through dimension decoupling, lightweight multi-semantic guidance, and semantic disparities mitigation. The experiments demonstrate that SCSA achieves superior performance in image classification, object detection, and semantic segmentation tasks. The SCSA module is also validated on complex scene detection datasets, showing its effectiveness in handling challenging scenarios such as small targets, dark environments, and infrared scenes. The results indicate that SCSA provides a practical trade-off between computational complexity and performance, making it a promising approach for various vision tasks.