关键词: 零样本学习, 多标签分类, 跨模态映射, 多示例学习

Abstract: Zero-shot multi-label image classification aims to tag images with multiple labels under the condition that the testing labels have no corresponding training samples. Previous studies show that the key to multi-label image classification technology is to make rational use of the label relationship that exists between multi-label image categories. The key issues that need to be addressed in the zero-shot multi-label image classification are to realize the model transfer from seen class to unseen class and utilize the label correlation to achieve classification of testing unseen classes. A deep instance differentiation algorithm is proposed for this challenging learning task. Specifically, visual features are first projected into semantic embedding space by use of deep embedding network, then an instance differentiation algorithm is employed to achieve multi-label classification in semantic space. Compared with existing algorithms on the popular Natural Scene and IAPRTC-12 datasets, the experimental results demonstrate the proposed method is advanced and effective, and the advance of embedding network is verified.

Key words: zero-shot learning, multi-label classification, cross-modal mapping, multi-instance learning