  We propose Sequential Feature Filtering Classifier (FFC), a simple but
effective classifier for convolutional neural networks (CNNs). With sequential
LayerNorm and ReLU, FFC zeroes out low-activation units and preserves
high-activation units. The sequential feature filtering process generates
multiple features, which are fed into a shared classifier for multiple outputs.
FFC can be applied to any CNNs with a classifier, and significantly improves
performances with negligible overhead. We extensively validate the efficacy of
FFC on various tasks: ImageNet-1K classification, MS COCO detection, Cityscapes
segmentation, and HMDB51 action recognition. Moreover, we empirically show that
FFC can further improve performances upon other techniques, including attention
modules and augmentation techniques. The code and models will be publicly
available.
