Multimedia Classification Using Bipolar Relation Graphs
Recent studies on category relations have shown the promising progress in addressing classification problems. Existing works independently consider the known relation and classifier optimization, and thus restrain the room for performance improvement. In this work, a new loss function is proposed to leverage the underlining relations among categories and classifiers. In addition, the bipolar relation (BR) graph is employed to formulate a general form for diverse relations. This bipolar graph is automatically learnt for reliving the constraints which may happen during the cost minimization. Extensive experiments on three benchmarks with various hypotheses and graphs demonstrate that our method can offer a significant performance improvement by jointly learning from both BR graph and hypothesis, in particular on a small training dataset scenario that suffers from severe overfitting problem.