  As natural images usually contain multiple objects, multi-label image
classification is more applicable "in the wild" than single-label
classification. However, exhaustively annotating images with every object of
interest is costly and time-consuming. We aim to train multi-label classifiers
from single-label annotations only. We show that adding a consistency loss,
ensuring that the predictions of the network are consistent over consecutive
training epochs, is a simple yet effective method to train multi-label
classifiers in a weakly supervised setting. We further extend this approach
spatially, by ensuring consistency of the spatial feature maps produced over
consecutive training epochs, maintaining per-class running-average heatmaps for
each training image. We show that this spatial consistency loss further
improves the multi-label mAP of the classifiers. In addition, we show that this
method overcomes shortcomings of the "crop" data-augmentation by recovering
correct supervision signal even when most of the single ground truth object is
cropped out of the input image by the data augmentation. We demonstrate gains
of the consistency and spatial consistency losses over the binary cross-entropy
baseline, and over competing methods, on MS-COCO and Pascal VOC. We also
demonstrate improved multi-label classification mAP on ImageNet-1K using the
ReaL multi-label validation set.
