Sequential Feature Filtering Classifier (IEEE Access)

Minseok Seo, Jaemin Lee, Jongchan Park, Daehan Kim and Dong-Geol

Abstract

In this study, we propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs). Using 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 transmitted to a shared classifier, yielding multiple outputs. FFC can be applied to any CNN with a classifier and it significantly improves the performance with negligible overhead. In this study, the efficacy of FFC is validated extensively on various tasks—ImageNet-1K classification, MS COCO detection, Cityscapes segmentation, and HMDB51 action recognition. Moreover, it is empirically established that FFC can further improve performances using additional techniques, including attention modules.

Datasets

• The datsets can be downloaded from

  ImageNet-1K

/IMAGENET/ILSVRC/Data/CLS-LOC/

• Directory tree

   DATA/
       ILSVRC/ 
             /Data
                   /CLS-LOC
                           /train
                           /val
       
   Sequential-Feature-Filtering-Classifier/
       (main.py)

Models

Note that our top performance was 76.97 as a result of multiple runs, but the average of the results of multiple runs was 76.84 on the paper.

METHOD	DATASET	ACC
ResNet-50	ImageNet-1K	75.80
ResNet-50+FFC	ImageNet-1K	76.97

train-test script

train

python main.py --ffc

test

python main.py --ffc --r