论文标题
顺序特征过滤分类器
Sequential Feature Filtering Classifier
论文作者
论文摘要
我们提出了顺序特征过滤分类器（FFC），这是一种简单但有效的卷积神经网络（CNN）的分类器。使用顺序分层和恢复，FFC归零低激活单元并保留高激活单元。顺序特征滤波过程生成多个功能，这些功能被馈入多个输出的共享分类器中。 FFC可以应用于具有分类器的任何CNN，并通过可忽略不计的开销可显着提高性能。我们广泛验证了FFC对各种任务的疗效：Imagenet-1K分类，COCO检测，CityScapes Sementation和HMDB51动作识别。此外，我们从经验上表明，FFC可以进一步改善其他技术的性能，包括注意模块和增强技术。代码和模型将公开可用。
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.
