Sharing model with multi-level feature representations

Hierarchical classification models have been proposed to achieve high accuracy by transferring effective information across the categories. One important challenge for this paradigm is to design what can be transferred across the categories. In this paper, we propose a novel method to learn a sharing model by taking advantage of multi-level feature representations. Unlike many of the existing methods which learn the sharing model based on identical feature space, multi-level feature detectors enable our model to capture rich visual information in hierarchical category structure. Moreover, hierarchical classifier parameters associated with multi-level feature representations are learned to model the visual correlation in the hierarchy. The experimental results on Caltech-256 dataset and ImageNet subset demonstrate that our method achieves excellent performance compared with some state-of-the-art methods, and shows the advantage of multi-level information transfer.