FMGNet: An efficient feature-multiplex group network for real-time vision task

Hao Zhang, Yongqiang Ma, Kaipeng Zhang, Nanning Zheng, Shenqi Lai

Abstract: Highlights•We propose a novel CFG block that effectively utilizes the feature redundancy before and after the PWConv without any accompanying activation function. And we introduce a more efficient attention mechanism, the CCA block, which is compatible with the CFG block.•We build a more efficient lightweight network FMGNet based on CFG block and CCA block. FMGNet achieves comparable performance with state-of-the-art models in image classification and even outperforms transformer-based models with lower latency.•Extensive experiments on wide tasks including object detection, human pose estimation, person re-identification, and semantic segmentation also indicate that our network is an excellent backbone.