Deep collaborative multi-task network: A human decision process inspired model for hierarchical image classification

Highlights：

• We propose a deep collaborative multi-task learning framework for hierarchical classification, where each prediction problem in the hierarchy is regarded as a sub-task to obtain multi-granularity intermediate predictions.

• To well utilize the relations among different sub-tasks, a novel fusion function is designed based on the confidence degree and the uncertainty degree, which can adaptively adjust the weights of the intermediate predictions from all the sub-tasks, acquiring better final predictions.

• We evaluate the performance of the proposed model on three image datasets. The experimental results demonstrate that considering the relations among different sub-tasks can improve the classification results, and our proposed model can achieve state-of-the-art performance.