Recent research has shown that attention mechanisms can help convolutional networks train and infer more efficiently and accurately. However, the current attention mechanism mainly focuses on the relationship between global features and ignores the relationship between local features. A feature filtering module (FFM) for convolutional neural networks is proposed in this paper. The FFM module uses attention mechanisms in both spatial and channel dimensions and fuses the attention feature maps of the two branches into a 3D attention feature map to help effective feature information flow more efficiently. Extensive tests on the CIFAR-100, and MS COCO show that FFM improves baseline network performance under various models and tasks, demonstrating FFM's versatility.

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