SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention
<|reference_start|>SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention: Channel and spatial attentions have respectively brought significant improvements in extracting feature dependencies and spatial structure relations for various downstream vision tasks. While their combination is more beneficial for leveraging their individual strengths, the synergy between channel and spatial attentions has not been fully explored, lacking in fully harness the synergistic potential of multi-semantic information for feature guidance and mitigation of semantic disparities. Our study attempts to reveal the synergistic relationship between spatial and channel attention at multiple semantic levels, proposing a novel Spatial and Channel Synergistic Attention module (SCSA). Our SCSA consists of two parts: 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. Additionally, the robust feature interactions based on the self-attention mechanism in PCSA further mitigate the disparities in multi-semantic information among different sub-features within SMSA. We conduct extensive experiments on seven benchmark datasets, including classification on ImageNet-1K, object detection on MSCOCO 2017, segmentation on ADE20K, and four other complex scene detection datasets. Our results demonstrate that our proposed SCSA not only surpasses the current state-of-the-art attention but also exhibits enhanced generalization capabilities across various task scenarios. The code and models are available at: https://github.com/HZAI-ZJNU/SCSA.<|reference_end|>
@article{si2024scsa:,
    title={SCSA: Exploring the Synergistic Effects Between Spatial and Channel
  Attention},
    author={Yunzhong Si, Huiying Xu, Xinzhong Zhu, Wenhao Zhang, Yao Dong, Yuxing
  Chen and Hongbo Li},
    journal={arXiv preprint arXiv:2407.05128},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05128},
    primaryClass={cs.CV}
}