Abstract

Pointwise label and pairwise label are both widely usedin computer vision tasks. For example, supervised im-age classification and annotation approaches use pointwiselabel, while attribute-based image relative learning oftenadopts pairwise labels. These two types of labels are of-ten considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with “coast” and annotated with “beach, sea, sand, sky” is more likely to have a higher ranking score in terms of the attribute“open”; while “men shoes” ranked highly on the attribute “formal” are likely to be annotated with “leather, lace up”than “buckle, fabric”. The existence of potential relationsbetween pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes; and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.