  Most classification models treat different object classes in parallel and the
misclassifications between any two classes are treated equally. In contrast,
human beings can exploit high-level information in making a prediction of an
unknown object. Inspired by this observation, the paper proposes a super-class
guided network (SGNet) to integrate the high-level semantic information into
the network so as to increase its performance in inference. SGNet takes
two-level class annotations that contain both super-class and finer class
labels. The super-classes are higher-level semantic categories that consist of
a certain amount of finer classes. A super-class branch (SCB), trained on
super-class labels, is introduced to guide finer class prediction. At the
inference time, we adopt two different strategies: Two-step inference (TSI) and
direct inference (DI). TSI first predicts the super-class and then makes
predictions of the corresponding finer class. On the other hand, DI directly
generates predictions from the finer class branch (FCB). Extensive experiments
have been performed on CIFAR-100 and MS COCO datasets. The experimental results
validate the proposed approach and demonstrate its superior performance on
image classification and object detection.
