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Convolutional Neural Networks (CNNs) excel in local spatial patternrecognition. For many vision tasks, such as object recognition andsegmentation, salient information is also present outside CNN's kernelboundaries. However, CNNs struggle in capturing such relevant information dueto their confined receptive fields. Self-attention can improve a model's accessto global information but increases computational overhead. We present a fastand simple fully convolutional method called DAS that helps focus attention onrelevant information. It uses deformable convolutions for the location ofpertinent image regions and separable convolutions for efficiency. DAS plugsinto existing CNNs and propagates relevant information using a gatingmechanism. Compared to the O(n^2) computational complexity of transformer-styleattention, DAS is O(n). Our claim is that DAS's ability to pay increasedattention to relevant features results in performance improvements when addedto popular CNNs for Image Classification and Object Detection. For example, DASyields an improvement on Stanford Dogs (4.47%), ImageNet (1.91%), and COCO AP(3.3%) with base ResNet50 backbone. This outperforms other CNN attentionmechanisms while using similar or less FLOPs. Our code will be publiclyavailable.

DAS: A Deformable Attention to Capture Salient Information in CNNs